ExpertPack MCP

Name: ExpertPack MCP
Author: brianhearn

by brianhearnversion 0.7.0i★ –iRuns on your computer

Registry nameio.github.brianhearn/ep-mcp

Install

Overview

Description from the maker (original English)

Serve ExpertPack knowledge packs over MCP with hybrid search, graph traversal and provenance.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: GEMINI_API_KEY, AZURE_OPENAI_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: GEMINI_API_KEY, AZURE_OPENAI_API_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "ep": {
      "command": "uvx",
      "args": [
        "ep-mcp",
        "serve",
        "--config",
        "your-value",
        "--transport",
        "stdio"
      ],
      "env": {
        "GEMINI_API_KEY": "your-value",
        "AZURE_OPENAI_API_KEY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add ep --env GEMINI_API_KEY=your-value --env AZURE_OPENAI_API_KEY=your-value -- uvx ep-mcp serve --config your-value --transport stdio

Fill in the empty values before running it.

This one needs values on its command line, so copy the settings and fill them in.

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "ep": {
      "command": "uvx",
      "args": [
        "ep-mcp",
        "serve",
        "--config",
        "your-value",
        "--transport",
        "stdio"
      ],
      "env": {
        "GEMINI_API_KEY": "your-value",
        "AZURE_OPENAI_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one needs values on its command line, so copy the settings and fill them in.

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "ep": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "ep-mcp",
        "serve",
        "--config",
        "your-value",
        "--transport",
        "stdio"
      ],
      "env": {
        "GEMINI_API_KEY": "your-value",
        "AZURE_OPENAI_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

GEMINI_API_KEY secret env
Gemini API key for embeddings (or set AZURE_OPENAI_API_KEY when using Azure OpenAI)
AZURE_OPENAI_API_KEY secret env
Azure OpenAI key, if the config uses the Azure embedding provider

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account brianhearn, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7266 servers in this category
Published to the registry more recently than 69% of the other 7266 servers in this category
Basic checks equal to or better than 100% of the other 7266 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is ExpertPack MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: GEMINI_API_KEY, AZURE_OPENAI_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.7.0) reached the registry 17 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago

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Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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](https://expertpack.ai) · [📦 ExpertPack Framework](https://github.com/brianhearn/expert-pack) · [📖 Architecture](ARCHITECTURE.md)

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). Split chunks keep the original span as content/span_hash, carry a computed line_range, and record a heading_path (0.7.0+)
Heading paths (0.7.0+): every hit carries heading_path (e.g. ["Routing", "Setup", "Ports"]) so an H3 hit keeps its H2 parent; ep_read(path, section=..., neighbors=true) reads just that section plus its previous/next siblings
Freshness (0.7.0+, ExpertPack Core 4.2): hits and ep_read carry verified_age_days, age_stale (verified_at older than the file's refresh or the manifest freshness.refresh_cycle), expired and expires_at (volatile TTL). stale keeps its old meaning — index drift (file changed since indexing, reconstruct mode only). Optional down-rank via retrieval.freshness_penalty (default 0.0 = off)
Skills over MCP (0.7.0+, opt-in): set mcp.skills.enabled: true in a pack's manifest.yaml to declare the io.modelcontextprotocol/skills extension (SEP-2640). Each pack becomes one skill at skill://{slug}/SKILL.md (generated) with every non-on_demand file served verbatim at skill://{slug}/{path}; skills/list / skills/get publish the manifest with SHA-256 digests, sizes, ttlMs and cacheScope. Host support is still thin, so the ep_* tools remain the primary path
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)
skill://{slug}/SKILL.md, skill://{slug}/{+path} (only when mcp.skills.enabled: true)
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)
Golden retrieval eval (0.7.0+): tests/eval/ — YAML query sets (expected best-first, must_not, category) scored as recall@1/3/5, MRR and must_not violations per pack and category. Skipped in the default run; pytest tests/eval -m golden --golden --packs-root with EP_EVAL_TODAY=2026-10-20 and optional EP_EVAL_MIN="recall@5=0.9,mrr=0.7", or python tests/eval/golden_eval.py --golden --today 2026-10-20
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](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 /.ep-mcp/ first.

The GET /search HTTP endpoint is available at:

GET /search?q= &pack= &n=10

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

with header Authorization: Bearer .

Environment Variables

Only the key for your configured embedding provider is required.

Variable	Required	Description
`GEMINI_API_KEY`	If `embedding.provider: gemini`	Gemini embedding API key
`AZURE_OPENAI_ENDPOINT`	If `embedding.provider: azure-openai`	Azure OpenAI resource endpoint
`AZURE_OPENAI_API_KEY`	If `embedding.provider: azure-openai`	Azure OpenAI API key
`EP_MCP_KEY_{SLUG}`	Optional	Per-pack API key override (uppercase slug, hyphens→underscores)
`EP_MCP_REMOTE_HOST`	Optional	Deploy target for `scripts/deploy.sh`
`EP_MCP_REMOTE_SRC`	Optional	Remote source path for `deploy.sh`
`EP_MCP_REMOTE_SITE_PKG`	Optional	Remote site-packages path for `deploy.sh`

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.

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

License

Author

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

ExpertPack MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

ep-mcp

Features

Quick Start

Installation

Configuration

Required for public Host headers (SDK v2 DNS-rebinding protection).

See DEPLOYMENT.md — omit and Streamable HTTP returns 421.

Running

Environment Variables

Tech Stack

Project Structure

Related

License

Author