ep-mcp

ep-mcp

Expertise-as-a-Service over the Model Context Protocol.

An MCP server that turns any ExpertPack into a live, queryable knowledge service. Any MCP-compatible agent (Claude Desktop, Cursor, Windsurf, Claude.ai, custom hosts) can connect and retrieve high-quality, provenance-rich domain expertise via standardized tools.

🌐 expertpack.ai · 📦 ExpertPack Framework · 📖 Architecture

97.6% retrieval hit rate / 0 full misses on a 22-question benchmark against a production v4.1 atomic-conceptual pack (658 chunks). Prior v4.0 baseline on the same pack and eval set: 65.1%.

Features

• Schema-aware hybrid retrieval: BM25 + vector search (sqlite-vec), metadata boosting, MMR re-ranking, adaptive threshold filtering, length penalty
• requires: expansion (schema v4.1 atomic-conceptual): When a top-K atom's frontmatter declares requires: [B, C], the referenced atoms are auto-appended to results as additional SearchResults flagged requires_expanded=True. Directional (A→B does not imply B→A), transitive up to 2 hops, capped at 3 atoms and 3,500 cumulative tokens, appended after the max_results slice so it never displaces top-K.
• Intent-aware routing: Automatic query classification (ENTITY/HOW/WHY/WHEN/GENERAL) adjusts vector/BM25 fusion weights per query — no LLM required
• Query embedding cache: SQLite-backed cache keyed by (model, dimension, sha256(query)) — warm query latency drops from ~4s (live Gemini API) to ~1ms
• Deep graph traversal: Optional multi-hop BFS expansion over knowledge graph edges with per-hop score decay and drift prevention
• Reserved-slot graph merge: Configurable protected slots in the final top-K reserved for graph-expanded neighbors so related files surface even against dominant core results
• BM25 fallback mode: Embedding provider outage degrades gracefully to lexical-only search (coverage + density + path + length scoring) instead of a hard error
• Cross-encoder reranker: Optional second-pass precision reranker (disabled by default, ~70ms warm latency when enabled)
• Provenance-first: Every result includes id, content_hash, verified_at, source_file
• Reconstruct mode (RFC-003): Opt-in reconstruct=true / reconstruct: true enriches results with fragment_id, line_range, original_markdown, span-level content_hash, excerpt, stale, and byte offsets so agents can prove exactly what source text grounded an answer
• EP-native chunking: Files are treated as atomic retrieval units (split at ## only for oversized content)
• Frontmatter aware: Strips metadata for embedding, extracts type, tags, requires, etc. for boosting/expansion/filtering
• Incremental indexing: Content-hash based staleness detection — only re-embeds changed files
• Multi-pack support: Path-based routing at /packs/{slug}/mcp
• Graph-aware retrieval: optional post-retrieval expansion via _graph.yaml adjacency, including accepted ontology/entity nodes when graph export provides them
• context_prefix (RFC-004): sidecar situating strings are prepended at index time for embed + BM25 only; returned/reconstruct text stays the original atom body
• Tier / navigation filter: retrieval_strategy: navigation, _index.md, source-coverage.md, and context.on_demand files are excluded from the RAG pool (still readable via ep_read / resources)
• Tools:
  • ep_search — primary hybrid retrieval tool
  • ep_read — load a whole atom by path or provenance id (consume-loop step 2)
  • ep_list_topics — browse pack structure and available content types
  • ep_graph_traverse
  • Multi-pack stdio support: All tools accept a pack parameter for routing when multiple packs are configured. Single-pack configs make pack optional (defaults to the only configured pack).
• Resources:
  • ep://{slug}/manifest
  • ep://{slug}/authority
  • ep://{slug}/files
  • ep://{slug}/file/{+path} (raw content with frontmatter)
• Transports: Streamable HTTP (primary, cloud-ready), stdio (local dev)
  • stdio multi-pack support (0.6.0+): When using --transport stdio with multiple packs configured, all tools accept an optional pack parameter (slug string) to specify which pack to query. For single-pack configurations, the pack parameter is optional and defaults to the only configured pack. This allows MCP hosts (Cursor, Claude Desktop) to use a single stdio process for multiple packs instead of requiring one process per pack.
• HTTP endpoint: GET /search (for non-MCP HTTP clients)
• Auth: API key (Phase 1), designed for OAuth 2.1 (Phase 2)

Quick Start

Installation

git clone https://github.com/brianhearn/ep-mcp.git
cd ep-mcp
pip install -e .[dev]

Configuration

Create config.yaml with at minimum:

server:
  host: "127.0.0.1"
  port: 8000
  log_level: "info"
  # Required for public Host headers (SDK v2 DNS-rebinding protection).
  # See DEPLOYMENT.md — omit and Streamable HTTP returns 421.
  mcp_allowed_hosts:
    - "localhost"
    - "localhost:*"
    - "127.0.0.1"
    - "127.0.0.1:*"

packs:
  - slug: "my-pack"
    path: "/path/to/your/expertpack"
    api_keys:
      - "your-secret-key-here"

embedding:
  provider: "gemini"           # "gemini" (default) or "azure-openai"
  model: "gemini-embedding-001"

For the full configuration reference — embedding providers, retrieval tuning (mmr_lambda, min_score, default_max_results, graph expansion, reranker, requires: expansion, etc.), production systemd setup, and deployment gotchas — see DEPLOYMENT.md.

Running

ep-mcp serve --config config.yaml

The server builds/updates the SQLite index (FTS5 + sqlite-vec) on startup. 0.5.0 requires a rebuild of existing indexes (index_features=context_prefix_v1+nav_filter_v1); startup does this automatically, or delete <pack>/.ep-mcp/ first.

The GET /search HTTP endpoint is available at:

GET /search?q=<query>&pack=<slug>&n=10

Add &reconstruct=true to include original markdown spans and provenance blocks for verification.

with header Authorization: Bearer <key>.

Environment Variables

Only the key for your configured embedding provider is required.

Tech Stack

• Language: Python 3.12+
• MCP Framework: official mcp SDK v2 (MCPServer, spec 2026-07-28, dual-era with 2025 clients)
• Storage: SQLite with FTS5 + sqlite-vec
• Embeddings: Gemini (default), configurable Azure OpenAI / OpenAI providers
• Web: Starlette + Uvicorn (for Streamable HTTP)
• CLI: Click

See pyproject.toml for exact dependencies.

Project Structure

See ARCHITECTURE.md for detailed module breakdown, retrieval pipeline, indexing strategy, and design decisions.

Related

• expertpack.ai — Framework website, community packs, and documentation
• ExpertPack — Schema framework, validation tools, Obsidian compatibility, community packs
• ExpertPacks — Published knowledge packs (private)
• EasyTerritory MCP (future) — Domain-specific layer built on top of this repo + ezt-designer pack

License

Apache 2.0 © 2026 Brian Hearn

Author

Brian Hearn — Built in 3 days (April 10–11, 2026).