844762f187
Add KB lifecycle management system with deduplication, consolidation, archival, and health analytics. Also fix pre-existing routing bug in upload_document MCP tool (was hitting /import instead of new /documents/content). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
686 lines
28 KiB
Python
686 lines
28 KiB
Python
#!/usr/bin/env python3
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"""
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True MCP Server for Claude Code integration.
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Wraps the Claude OS REST API as a proper MCP server with stdio transport.
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Server name is "code-forge" for backwards compatibility with existing
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memories and documentation, but it IS Claude OS.
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Usage:
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Run: ./install.sh (this will configure everything)
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Or manually add to ~/.claude/settings.json:
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{
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"mcpServers": {
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"code-forge": {
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"command": "/path/to/claude-os/venv/bin/python3",
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"args": ["/path/to/claude-os/mcp_server/claude_code_mcp.py"],
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"env": {
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"CLAUDE_OS_API": "http://localhost:8051"
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}
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}
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}
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}
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"""
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import asyncio
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import json
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import os
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import sys
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from typing import Any
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import httpx
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from mcp.server import Server
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from mcp.server.stdio import stdio_server
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from mcp.types import Tool, TextContent
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# Configuration
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API_BASE = os.environ.get("CLAUDE_OS_API", "http://localhost:8051")
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# Initialize MCP server - named "code-forge" for backwards compatibility
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server = Server("code-forge")
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def api_url(path: str) -> str:
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"""Build full API URL."""
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return f"{API_BASE}{path}"
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async def api_get(path: str) -> dict:
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"""Make GET request to API."""
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async with httpx.AsyncClient() as client:
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response = await client.get(api_url(path), timeout=30.0)
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response.raise_for_status()
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return response.json()
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async def api_post(path: str, data: dict = None) -> dict:
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"""Make POST request to API."""
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async with httpx.AsyncClient() as client:
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response = await client.post(api_url(path), json=data or {}, timeout=60.0)
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response.raise_for_status()
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return response.json()
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async def api_delete(path: str) -> dict:
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"""Make DELETE request to API."""
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async with httpx.AsyncClient() as client:
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response = await client.delete(api_url(path), timeout=30.0)
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response.raise_for_status()
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return response.json()
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async def api_put(path: str, data: dict = None) -> dict:
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"""Make PUT request to API."""
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async with httpx.AsyncClient() as client:
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response = await client.put(api_url(path), json=data or {}, timeout=30.0)
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response.raise_for_status()
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return response.json()
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@server.list_tools()
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async def list_tools() -> list[Tool]:
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"""List available Claude OS tools.
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Tool names match what templates/commands expect:
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- mcp__code-forge__search_knowledge_base
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- mcp__code-forge__list_knowledge_bases
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- mcp__code-forge__list_documents
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- etc.
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"""
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return [
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# Knowledge Base Tools
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Tool(
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name="list_knowledge_bases",
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description="List all knowledge bases in Claude OS",
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inputSchema={
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"type": "object",
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"properties": {},
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"required": []
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}
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),
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Tool(
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name="create_knowledge_base",
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description="Create a new knowledge base",
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inputSchema={
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"type": "object",
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"properties": {
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"name": {"type": "string", "description": "Name of the knowledge base"},
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"kb_type": {"type": "string", "enum": ["generic", "code", "documentation"], "default": "generic"},
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"description": {"type": "string", "description": "Description of the KB"}
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},
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"required": ["name"]
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}
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),
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Tool(
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name="search_knowledge_base",
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description="Search/query a knowledge base using RAG. Returns relevant documents and an AI-generated answer.",
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inputSchema={
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"type": "object",
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"properties": {
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"kb_name": {"type": "string", "description": "Name of the knowledge base to search"},
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"query": {"type": "string", "description": "Search query"},
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"top_k": {"type": "integer", "default": 5, "description": "Number of results to return"},
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"use_hybrid": {"type": "boolean", "default": True, "description": "Use hybrid search (semantic + keyword)"}
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},
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"required": ["kb_name", "query"]
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}
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),
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Tool(
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name="get_kb_stats",
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description="Get statistics for a knowledge base (document count, chunk count, etc.)",
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inputSchema={
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"type": "object",
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"properties": {
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"kb_name": {"type": "string", "description": "Name of the knowledge base"}
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},
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"required": ["kb_name"]
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}
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),
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Tool(
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name="list_documents",
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description="List all documents in a knowledge base",
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inputSchema={
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"type": "object",
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"properties": {
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"kb_name": {"type": "string", "description": "Name of the knowledge base"}
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},
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"required": ["kb_name"]
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}
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),
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Tool(
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name="delete_knowledge_base",
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description="Delete a knowledge base and all its documents",
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inputSchema={
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"type": "object",
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"properties": {
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"kb_name": {"type": "string", "description": "Name of the knowledge base to delete"}
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},
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"required": ["kb_name"]
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}
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),
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# Project Tools
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Tool(
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name="list_projects",
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description="List all projects registered with Claude OS",
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inputSchema={
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"type": "object",
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"properties": {},
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"required": []
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}
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),
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Tool(
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name="create_project",
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description="Create/register a new project with Claude OS",
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inputSchema={
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"type": "object",
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"properties": {
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"name": {"type": "string", "description": "Project name"},
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"path": {"type": "string", "description": "Absolute path to project directory"},
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"description": {"type": "string", "description": "Project description"}
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},
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"required": ["name", "path"]
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}
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),
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Tool(
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name="get_project",
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description="Get details for a specific project",
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inputSchema={
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"type": "object",
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"properties": {
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"project_id": {"type": "integer", "description": "Project ID"}
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},
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"required": ["project_id"]
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}
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),
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# Indexing Tools
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Tool(
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name="index_structural",
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description="Run tree-sitter structural indexing on code files. Fast indexing that extracts functions, classes, imports.",
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inputSchema={
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"type": "object",
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"properties": {
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"kb_name": {"type": "string", "description": "Name of the knowledge base"},
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"path": {"type": "string", "description": "Path to directory to index"}
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},
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"required": ["kb_name", "path"]
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}
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),
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Tool(
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name="index_semantic",
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description="Run semantic embedding indexing on a knowledge base. Creates vector embeddings for similarity search. Runs in background by default - returns job_id to check progress at /api/jobs/{job_id}.",
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inputSchema={
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"type": "object",
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"properties": {
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"kb_name": {"type": "string", "description": "Name of the knowledge base"},
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"project_path": {"type": "string", "description": "Path to project to index"},
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"selective": {"type": "boolean", "description": "If true, only index top 20% most important files + docs (default: true)"},
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"background": {"type": "boolean", "description": "If true, run in background (default: true). Set false to block until complete."}
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},
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"required": ["kb_name", "project_path"]
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}
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),
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# Document Management
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Tool(
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name="upload_document",
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description="Upload/save a document or memory to a knowledge base",
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inputSchema={
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"type": "object",
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"properties": {
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"kb_name": {"type": "string", "description": "Name of the knowledge base"},
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"content": {"type": "string", "description": "Content to save (markdown supported)"},
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"filename": {"type": "string", "description": "Filename for the document (e.g., 'my-memory.md')"},
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"title": {"type": "string", "description": "Title for the document"},
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"tags": {"type": "array", "items": {"type": "string"}, "description": "Tags for categorization"}
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},
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"required": ["kb_name", "content", "filename"]
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}
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),
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Tool(
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name="delete_document",
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description="Delete a document from a knowledge base",
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inputSchema={
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"type": "object",
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"properties": {
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"kb_name": {"type": "string", "description": "Name of the knowledge base"},
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"filename": {"type": "string", "description": "Filename of the document to delete"}
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},
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"required": ["kb_name", "filename"]
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}
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),
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# Utility Tools
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Tool(
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name="get_ollama_models",
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description="List available Ollama models for embeddings and LLM",
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inputSchema={
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"type": "object",
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"properties": {},
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"required": []
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}
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),
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Tool(
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name="health_check",
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description="Check if Claude OS API server is running and healthy",
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inputSchema={
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"type": "object",
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"properties": {},
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"required": []
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}
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),
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# Session Tools
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Tool(
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name="list_sessions",
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description="List Claude Code session files for a project. Sessions are stored in ~/.claude/projects/{encoded-path}/ and contain conversation history.",
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inputSchema={
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"type": "object",
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"properties": {
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"project_path": {"type": "string", "description": "Absolute path to project directory (e.g., /Users/x/Projects/myapp)"},
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"limit": {"type": "integer", "default": 50, "description": "Maximum sessions to return (default: 50)"}
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},
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"required": ["project_path"]
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}
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),
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Tool(
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name="extract_session_insights",
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description="Extract insights from a Claude Code session file using LLM. Analyzes conversation to find decisions, patterns, solutions, and blockers. Optionally saves insights to a knowledge base.",
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inputSchema={
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"type": "object",
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"properties": {
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"session_path": {"type": "string", "description": "Absolute path to .jsonl session file"},
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"kb_name": {"type": "string", "description": "Knowledge base name to save insights (optional)"},
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"auto_save": {"type": "boolean", "default": False, "description": "Auto-save all insights without prompting"},
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"insight_types": {
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"type": "array",
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"items": {"type": "string", "enum": ["decision", "pattern", "solution", "blocker"]},
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"default": ["decision", "pattern", "solution", "blocker"],
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"description": "Types of insights to extract"
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},
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"min_confidence": {"type": "number", "default": 0.7, "description": "Minimum confidence threshold (0.0-1.0)"}
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},
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"required": ["session_path"]
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}
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),
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# Skill Management Tools
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Tool(
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name="list_skills",
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description="List all skills (global and project-level). Global skills are in ~/.claude/skills/, project skills are in {project}/.claude/skills/.",
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inputSchema={
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"type": "object",
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"properties": {
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"project_path": {"type": "string", "description": "Project path to include project-level skills (optional)"},
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"include_content": {"type": "boolean", "default": False, "description": "Include full skill content"}
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},
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"required": []
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}
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),
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Tool(
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name="list_skill_templates",
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description="List available skill templates that can be installed to projects. Templates are organized by category.",
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inputSchema={
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"type": "object",
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"properties": {
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"category": {"type": "string", "description": "Filter by category (optional)"}
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},
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"required": []
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}
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),
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Tool(
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name="get_skill",
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description="Get skill details including full content.",
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inputSchema={
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"type": "object",
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"properties": {
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"name": {"type": "string", "description": "Skill name"},
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"scope": {"type": "string", "enum": ["global", "project"], "description": "Where the skill is located"},
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"project_path": {"type": "string", "description": "Project path (required if scope is 'project')"}
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},
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"required": ["name", "scope"]
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}
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),
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Tool(
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name="install_skill_template",
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description="Install a skill template to a project. Copies the template to {project}/.claude/skills/.",
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inputSchema={
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"type": "object",
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"properties": {
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"template_name": {"type": "string", "description": "Name of template to install"},
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"project_path": {"type": "string", "description": "Project path to install to"},
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"custom_name": {"type": "string", "description": "Custom name for installed skill (optional)"}
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},
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"required": ["template_name", "project_path"]
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}
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),
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Tool(
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name="create_skill",
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description="Create a custom skill for a project.",
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inputSchema={
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"type": "object",
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"properties": {
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"name": {"type": "string", "description": "Skill name"},
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"description": {"type": "string", "description": "Skill description"},
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"content": {"type": "string", "description": "Skill content (markdown)"},
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"project_path": {"type": "string", "description": "Project path"},
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"category": {"type": "string", "description": "Category (optional)"},
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"tags": {"type": "array", "items": {"type": "string"}, "description": "Tags (optional)"}
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},
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"required": ["name", "description", "content", "project_path"]
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}
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),
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Tool(
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name="update_skill",
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description="Update an existing project skill.",
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inputSchema={
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"type": "object",
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"properties": {
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"name": {"type": "string", "description": "Skill name"},
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"project_path": {"type": "string", "description": "Project path"},
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"content": {"type": "string", "description": "New content (optional)"},
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"description": {"type": "string", "description": "New description (optional)"},
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"tags": {"type": "array", "items": {"type": "string"}, "description": "New tags (optional)"}
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},
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"required": ["name", "project_path"]
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}
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),
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Tool(
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name="delete_skill",
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description="Delete a project skill. Cannot delete global skills.",
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inputSchema={
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"type": "object",
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"properties": {
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"name": {"type": "string", "description": "Skill name to delete"},
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"project_path": {"type": "string", "description": "Project path"}
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},
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"required": ["name", "project_path"]
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}
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),
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# Knowledge Lifecycle Tools
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Tool(
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name="kb_lifecycle_health",
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description="Get a comprehensive health report for a knowledge base including duplicate detection, embedding coverage, age distribution, and recommendations.",
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inputSchema={
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"type": "object",
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"properties": {
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"kb_name": {"type": "string", "description": "Name of the knowledge base"}
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},
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"required": ["kb_name"]
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}
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),
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Tool(
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name="kb_lifecycle_dedup",
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description="Scan for and merge duplicate documents in a knowledge base. Use action='scan' to find duplicates, action='merge' to merge them.",
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inputSchema={
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"type": "object",
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"properties": {
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"kb_name": {"type": "string", "description": "Name of the knowledge base"},
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"action": {"type": "string", "enum": ["scan", "merge"], "description": "Action to perform: scan for duplicates or merge them"},
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"threshold": {"type": "number", "default": 0.85, "description": "Similarity threshold for scan (0.0-1.0)"},
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"max_pairs": {"type": "integer", "default": 100, "description": "Maximum duplicate pairs to return"},
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"keep_doc_id": {"type": "string", "description": "Document ID to keep (required for merge)"},
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"remove_doc_ids": {"type": "array", "items": {"type": "string"}, "description": "Document IDs to remove (required for merge)"},
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"dry_run": {"type": "boolean", "default": False, "description": "Preview changes without applying"}
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},
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"required": ["kb_name", "action"]
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}
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),
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Tool(
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name="kb_lifecycle_consolidate",
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description="Consolidate multiple related documents into a single merged document using LLM-powered summarization.",
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inputSchema={
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"type": "object",
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"properties": {
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"kb_name": {"type": "string", "description": "Name of the knowledge base"},
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"doc_ids": {"type": "array", "items": {"type": "string"}, "description": "Document IDs to consolidate"},
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"new_filename": {"type": "string", "description": "Filename for the consolidated document"},
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"dry_run": {"type": "boolean", "default": False, "description": "Preview without consolidating"}
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},
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"required": ["kb_name", "doc_ids", "new_filename"]
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}
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),
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Tool(
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name="kb_lifecycle_archive",
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description="Manage document archival: archive, restore, list archived, or find stale documents.",
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inputSchema={
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"type": "object",
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"properties": {
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"kb_name": {"type": "string", "description": "Name of the knowledge base"},
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"action": {"type": "string", "enum": ["archive", "restore", "list", "stale"], "description": "Archival action to perform"},
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"doc_ids": {"type": "array", "items": {"type": "string"}, "description": "Document IDs (required for archive/restore)"},
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"reason": {"type": "string", "default": "manual", "description": "Reason for archiving"},
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"stale_days": {"type": "integer", "default": 90, "description": "Days threshold for stale detection"}
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},
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"required": ["kb_name", "action"]
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}
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)
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]
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@server.call_tool()
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async def call_tool(name: str, arguments: dict[str, Any]) -> list[TextContent]:
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"""Execute a Claude OS tool."""
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try:
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result = await _execute_tool(name, arguments)
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return [TextContent(type="text", text=json.dumps(result, indent=2))]
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except httpx.HTTPStatusError as e:
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error_msg = f"API error: {e.response.status_code} - {e.response.text}"
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return [TextContent(type="text", text=json.dumps({"error": error_msg}))]
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except httpx.ConnectError:
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return [TextContent(type="text", text=json.dumps({
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"error": "Cannot connect to Claude OS API. Is the server running? Start with: ./start_all_services.sh"
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}))]
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except Exception as e:
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return [TextContent(type="text", text=json.dumps({"error": str(e)}))]
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async def _execute_tool(name: str, args: dict[str, Any]) -> dict:
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"""Route tool calls to appropriate API endpoints."""
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# Knowledge Base Tools
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if name == "list_knowledge_bases":
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return await api_get("/api/kb")
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elif name == "create_knowledge_base":
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return await api_post("/api/kb", {
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"name": args["name"],
|
|
"kb_type": args.get("kb_type", "generic"),
|
|
"description": args.get("description", "")
|
|
})
|
|
|
|
elif name == "search_knowledge_base":
|
|
return await api_post(f"/api/kb/{args['kb_name']}/chat", {
|
|
"query": args["query"],
|
|
"top_k": args.get("top_k", 5),
|
|
"use_hybrid": args.get("use_hybrid", True)
|
|
})
|
|
|
|
elif name == "get_kb_stats":
|
|
return await api_get(f"/api/kb/{args['kb_name']}/stats")
|
|
|
|
elif name == "list_documents":
|
|
return await api_get(f"/api/kb/{args['kb_name']}/documents")
|
|
|
|
elif name == "delete_knowledge_base":
|
|
return await api_delete(f"/api/kb/{args['kb_name']}")
|
|
|
|
# Project Tools
|
|
elif name == "list_projects":
|
|
return await api_get("/api/projects")
|
|
|
|
elif name == "create_project":
|
|
return await api_post("/api/projects", {
|
|
"name": args["name"],
|
|
"path": args["path"],
|
|
"description": args.get("description", "")
|
|
})
|
|
|
|
elif name == "get_project":
|
|
return await api_get(f"/api/projects/{args['project_id']}")
|
|
|
|
# Indexing Tools
|
|
elif name == "index_structural":
|
|
return await api_post(f"/api/kb/{args['kb_name']}/index-structural", {
|
|
"path": args["path"]
|
|
})
|
|
|
|
elif name == "index_semantic":
|
|
return await api_post(f"/api/kb/{args['kb_name']}/index-semantic", {
|
|
"project_path": args["project_path"],
|
|
"selective": args.get("selective", True),
|
|
"background": args.get("background", True)
|
|
})
|
|
|
|
# Document Management
|
|
elif name == "upload_document":
|
|
return await api_post(f"/api/kb/{args['kb_name']}/documents/content", {
|
|
"content": args["content"],
|
|
"filename": args["filename"],
|
|
"metadata": {
|
|
"title": args.get("title", ""),
|
|
"tags": args.get("tags", [])
|
|
}
|
|
})
|
|
|
|
elif name == "delete_document":
|
|
return await api_delete(f"/api/kb/{args['kb_name']}/documents/{args['filename']}")
|
|
|
|
# Utility Tools
|
|
elif name == "get_ollama_models":
|
|
return await api_get("/api/ollama/models")
|
|
|
|
elif name == "health_check":
|
|
try:
|
|
# Simple health check - list KBs should work if server is up
|
|
result = await api_get("/api/kb")
|
|
return {"status": "healthy", "message": "Claude OS is running", "kb_count": len(result.get("knowledge_bases", []))}
|
|
except Exception as e:
|
|
return {"status": "unhealthy", "error": str(e)}
|
|
|
|
# Session Tools
|
|
elif name == "list_sessions":
|
|
return await api_get(f"/api/sessions/list?project_path={args['project_path']}&limit={args.get('limit', 50)}")
|
|
|
|
elif name == "extract_session_insights":
|
|
return await api_post("/api/sessions/extract", {
|
|
"session_path": args["session_path"],
|
|
"kb_name": args.get("kb_name"),
|
|
"auto_save": args.get("auto_save", False),
|
|
"insight_types": args.get("insight_types"),
|
|
"min_confidence": args.get("min_confidence", 0.7)
|
|
})
|
|
|
|
# Skill Management Tools
|
|
elif name == "list_skills":
|
|
project_path = args.get("project_path", "")
|
|
include_content = args.get("include_content", False)
|
|
url = f"/api/skills?include_content={include_content}"
|
|
if project_path:
|
|
url += f"&project_path={project_path}"
|
|
return await api_get(url)
|
|
|
|
elif name == "list_skill_templates":
|
|
category = args.get("category")
|
|
url = "/api/skills/templates"
|
|
if category:
|
|
url += f"?category={category}"
|
|
return await api_get(url)
|
|
|
|
elif name == "get_skill":
|
|
scope = args["scope"]
|
|
name_param = args["name"]
|
|
project_path = args.get("project_path", "")
|
|
url = f"/api/skills/{scope}/{name_param}"
|
|
if project_path:
|
|
url += f"?project_path={project_path}"
|
|
return await api_get(url)
|
|
|
|
elif name == "install_skill_template":
|
|
return await api_post(f"/api/skills/install?project_path={args['project_path']}", {
|
|
"template_name": args["template_name"],
|
|
"custom_name": args.get("custom_name")
|
|
})
|
|
|
|
elif name == "create_skill":
|
|
return await api_post(f"/api/skills?project_path={args['project_path']}", {
|
|
"name": args["name"],
|
|
"description": args["description"],
|
|
"content": args["content"],
|
|
"category": args.get("category"),
|
|
"tags": args.get("tags")
|
|
})
|
|
|
|
elif name == "update_skill":
|
|
return await api_put(f"/api/skills/{args['name']}?project_path={args['project_path']}", {
|
|
"content": args.get("content"),
|
|
"description": args.get("description"),
|
|
"tags": args.get("tags")
|
|
})
|
|
|
|
elif name == "delete_skill":
|
|
return await api_delete(f"/api/skills/{args['name']}?project_path={args['project_path']}")
|
|
|
|
# Knowledge Lifecycle Tools
|
|
elif name == "kb_lifecycle_health":
|
|
return await api_get(f"/api/kb/{args['kb_name']}/lifecycle/health")
|
|
|
|
elif name == "kb_lifecycle_dedup":
|
|
action = args["action"]
|
|
kb_name = args["kb_name"]
|
|
if action == "scan":
|
|
return await api_post(f"/api/kb/{kb_name}/lifecycle/dedup-scan", {
|
|
"threshold": args.get("threshold", 0.85),
|
|
"max_pairs": args.get("max_pairs", 100)
|
|
})
|
|
elif action == "merge":
|
|
return await api_post(f"/api/kb/{kb_name}/lifecycle/dedup-merge", {
|
|
"keep_doc_id": args["keep_doc_id"],
|
|
"remove_doc_ids": args["remove_doc_ids"],
|
|
"dry_run": args.get("dry_run", False)
|
|
})
|
|
else:
|
|
return {"error": f"Unknown dedup action: {action}"}
|
|
|
|
elif name == "kb_lifecycle_consolidate":
|
|
return await api_post(f"/api/kb/{args['kb_name']}/lifecycle/consolidate", {
|
|
"doc_ids": args["doc_ids"],
|
|
"new_filename": args["new_filename"],
|
|
"dry_run": args.get("dry_run", False)
|
|
})
|
|
|
|
elif name == "kb_lifecycle_archive":
|
|
action = args["action"]
|
|
kb_name = args["kb_name"]
|
|
if action == "archive":
|
|
return await api_post(f"/api/kb/{kb_name}/lifecycle/archive", {
|
|
"doc_ids": args["doc_ids"],
|
|
"reason": args.get("reason", "manual")
|
|
})
|
|
elif action == "restore":
|
|
return await api_post(f"/api/kb/{kb_name}/lifecycle/restore", {
|
|
"doc_ids": args["doc_ids"]
|
|
})
|
|
elif action == "list":
|
|
return await api_get(f"/api/kb/{kb_name}/lifecycle/archived")
|
|
elif action == "stale":
|
|
stale_days = args.get("stale_days", 90)
|
|
return await api_get(f"/api/kb/{kb_name}/lifecycle/stale?stale_days={stale_days}")
|
|
else:
|
|
return {"error": f"Unknown archive action: {action}"}
|
|
|
|
else:
|
|
return {"error": f"Unknown tool: {name}"}
|
|
|
|
|
|
async def main():
|
|
"""Run the MCP server."""
|
|
async with stdio_server() as (read_stream, write_stream):
|
|
await server.run(read_stream, write_stream, server.create_initialization_options())
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|