Fraim Context MCP
Semantic search MCP server for project documentation.
Version: 5.1.0
Status: In Development
Overview
Fraim Context MCP exposes project documentation to LLMs via the Model Context Protocol (MCP). It supports:
- Fast mode: Direct cache/search for immediate results
- Deep mode: Multi-round synthesis for complex queries
- Hybrid search: Vector similarity + full-text search with pgvector
- Smart caching: Redis with corpus versioning for cache invalidation
Quick Start
# 1. Setup Doppler
doppler login
doppler setup # Select: fraim-context → dev
# 2. Install dependencies
uv sync
# 3. Verify environment
doppler run -- uv run python scripts/verify_env.py
# 4. Run tests
doppler run -- uv run pytest tests/stage_0/ -v
Development
This project uses Test-Driven Development (TDD). See DNA/DEVELOPMENT_PLAN.md for stages.
# Run all tests
doppler run -- uv run pytest tests/ -v
# Run specific stage
doppler run -- uv run pytest tests/stage_0/ -v
# Lint
uv run ruff check src/ tests/
# Type check
uv run mypy src/fraim_mcp
Architecture
- LLM Access: Pydantic AI Gateway (unified key for all providers)
- Database: PostgreSQL + pgvector (1024-dim embeddings)
- Cache: Redis 7.x (native asyncio)
- Observability: Logfire (OpenTelemetry)
See DNA/specs/ARCHITECTURE.md for full details.
License
MIT