本地文本搜索服务器

# Install basic version (keyword search)
uvx install context42

# Install with CLaRa semantic compression
pip install context42[clara]

# Run directly
uvx context42

# Or via uv
uv run context42

# Download CLaRa model (optional)
context42 download

# 1. Initialize CLaRa (downloads model if needed)
tools/call init_clara {"model": "clara-7b-instruct-16"}

# 2. Load documents
tools/call load_documents {"directory": "./docs"}

# 3. Ask semantic questions
tools/call ask {"question": "What is the main topic of these documents?"}

# 4. Search with CLaRa (auto-enabled when loaded)
tools/call search {"query": "machine learning", "method": "clara"}

# 5. Check model status
tools/call clara_status {}

# 1. Load documents
tools/call load_documents {"directory": "./docs", "extensions": [".md", ".txt"]}

# 2. Apply 16x compression (100-char chunks)
tools/call chunk_documents {"compression_level": 16.0}

# 3. Search for content
tools/call search {"query": "machine learning", "top_k": 5}

# Claude Desktop
echo '{"mcpServers":{"context42":{"command":"uvx","args":["context42"]}}' >> ~/Library/Application\ Support/Claude/claude_desktop_config.json

# Cursor Editor
echo '{"mcpServers":{"context42":{"command":"uvx","args":["context42"]}}' > ~/.cursor/mcp_settings.json

# Continue.dev
echo '{"mcpServers":{"context42":{"command":"uvx","args":["context42"]}}' > ~/.continue/config.json

{
  "mcpServers": {
    "context42": {
      "command": "uvx",
      "args": ["context42"]
    }
  }
}

# Add to user scope (cross-project)
claude mcp add --transport stdio context42 -- uvx context42

# Add to project scope (team-shared)
claude mcp add --transport stdio context42 --scope project -- uvx context42

# Add with environment variables
claude mcp add --transport stdio context42 --env CONTEXT42_DOCS_PATH=/path/to/docs -- uvx context42

{
  "mcp": {
    "servers": {
      "context42": {
        "type": "stdio",
        "command": "uvx",
        "args": ["context42"]
      }
    }
  }
}

{
  "mcpServers": {
    "context42": {
      "command": "uvx",
      "args": ["context42"]
    }
  }
}

{
  "context_servers": {
    "Context42": {
      "source": "custom",
      "command": "uvx",
      "args": ["context42"]
    }
  }
}

{
  "mcpServers": {
    "context42": {
      "type": "stdio",
      "command": "uvx",
      "args": ["context42"]
    }
  }
}

{
  "mcpServers": {
    "context42": {
      "command": "uvx",
      "args": ["context42"],
      "env": {
        "CONTEXT42_DOCS_PATH": "${DOCS_PATH:-./docs}",
        "CONTEXT42_COMPRESSION": "${COMPRESSION_LEVEL:-4.0}"
      }
    }
  }
}

{
  "mcpServers": {
    "context42": {
      "command": "cmd",
      "args": ["/c", "uvx", "context42"]
    }
  }
}

# Reference server status
@context42://status

# Reference loaded documents
@context42://documents

/mcp__context42__load_docs
/mcp__context42__search
/mcp__context42__status

from mcp.client.stdio import stdio_client, StdioServerParameters
from mcp import ClientSession

async def use_context42():
    server_params = StdioServerParameters(
        command="uvx", args=["context42"]
    )
    
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()
            result = await session.call_tool("load_documents", {
                "directory": "./documents"
            })
            
            # Use CLaRa if available
            clara_result = await session.call_tool("init_clara", {
                "model": "clara-7b-instruct-16"
            })
            
            # Ask semantic questions
            answer = await session.call_tool("ask", {
                "question": "What is the main topic?",
                "max_tokens": 128
            })

# Model management (requires context42[clara])
context42 download                    # Download default CLaRa model
context42 models --local               # List downloaded models
context42 info clara-7b-instruct-16   # Show model details
context42 remove clara-7b-instruct-128  # Remove model

# Server management
context42 serve                       # Start MCP server
context42 serve --model clara-7b-instruct-128  # Use specific model
context42 serve --preload              # Preload model at startup
context42 serve --fallback             # Keyword-only mode

# List all configured MCP servers
claude mcp list

# Get Context42 server details
claude mcp get context42

# Remove Context42 server
claude mcp remove context42

# Check server status in Claude Code
/mcp

# Use Claude Code as MCP server (for testing)
claude mcp serve

// managed-mcp.json
{
  "mcpServers": {
    "context42": {
      "command": "uvx",
      "args": ["context42"],
      "env": {
        "CONTEXT42_DOCS_PATH": "/company/docs",
        "CONTEXT42_MAX_FILES": "10000",
        "CONTEXT42_MODEL": "clara-7b-instruct-16",
        "CONTEXT42_DEVICE": "cuda"
      }
    }
  },
  "allowedMcpServers": [
    {"serverName": "context42"}
  ]
}

# Install dev dependencies
uv sync

# Run server for testing
uv run context42

# Test via FastMCP inspector
fastmcp dev context42/server.py

context42/
├── __init__.py          # Package exports, CLARA_AVAILABLE flag
├── server.py            # FastMCP server with MCP tools
├── cli.py               # CLI: serve, download, models, remove, info
├── processor.py         # DocumentProcessor class
├── chunker.py           # Chunker class
├── search.py            # SearchEngine (keyword fallback)
└── clara/               # Optional CLaRa integration
    ├── __init__.py      # CLaRa exports
    ├── config.py        # CLaRaConfig with model registry
    ├── manager.py       # ModelManager (download/load/unload/remove)
    └── generator.py     # CLaRaGenerator (ask/search)

uv sync  # Install dependencies
uvx --install context42  # Ensure global installation