Context Graph MCP Server

MCP server for storing and querying decision traces with semantic search using Voyage AI embeddings and ChromaDB.

Features

• Semantic Search: Find decisions by meaning, not keywords

• Vector Embeddings: 1024-dim embeddings via Voyage AI

• Local Storage: ChromaDB for cross-platform vector database

• Outcome Tracking: Mark decisions as success/failure after validation

• Category Filtering: Group by framework, architecture, api, error, testing, deployment

Related MCP server: Memory MCP

Installation

# Install dependencies
pip install -r requirements.txt

# Set Voyage AI API key
export VOYAGE_API_KEY="your_key_here"

Usage

# Run server (stdio transport)
python server.py

MCP Configuration

Add to ~/.config/claude/mcp.json or .claude/mcp.json:

{
  "mcpServers": {
    "context-graph": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/context-graph-mcp",
        "run",
        "python",
        "server.py"
      ],
      "env": {
        "VOYAGE_API_KEY": "your_key_here"
      }
    }
  }
}

Tools

Trace Schema

{
  "id": "trace_abc123...",
  "timestamp": "2025-01-15T10:30:00",
  "category": "framework",
  "decision": "Chose FastAPI over Flask for async support",
  "outcome": "pending|success|failure",
  "state": "IMPLEMENT",
  "feature_id": "feat-001"
}

Categories

• framework - Tech stack choices

• architecture - Design patterns, structure

• api - Endpoint design, contracts

• error - Failure modes, fixes

• testing - Test strategies

• deployment - Infra decisions