ExpertPack MCP

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

What does this entry provide?

ExpertPack MCP is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 78/100. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding ExpertPack MCP to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.

Snapshot facts

Source: Official MCP Registry
Quality: 78/100
Install method: pypi
Repo: brianhearn/ep-mcp
Repo updated: Oct 20, 2026

Install command

uvx ep-mcp==0.7.0

Install method: pypi

Review the upstream source before running this command.

GitHub project Homepage

Quality

Repo stars

Repo updated

Oct 20, 2026

How is this score calculated?

Weighted across six dimensions, scored 0-100.
Provenance (25): listed in an official or curated registry.
Installability (25): a runnable install command beats a declared transport, which beats a source checkout.
Maintenance (20): recency of the last push; archived entries score zero here.
Adoption (20): repository stars on a log curve.

MCP manifest

{
  "_meta": {
    "io.modelcontextprotocol.registry/official": {
      "status": "active",
      "isLatest": true,
      "updatedAt": "2026-10-20T17:10:49.535454Z",
      "publishedAt": "2026-10-20T17:10:49.535454Z",
      "statusChangedAt": "2026-10-20T17:10:49.535454Z"
    }
  },
  "server": {
    "name": "io.github.brianhearn/ep-mcp",
    "title": "ExpertPack MCP",
    "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
    "version": "0.7.0",
    "packages": [
      {
        "version": "0.7.0",
        "transport": {
          "type": "stdio"
        },
        "identifier": "ep-mcp",
        "runtimeHint": "uvx",
        "registryType": "pypi",
        "registryBaseUrl": "https://pypi.org",
        "packageArguments": [
          {
            "type": "positional",
            "value": "serve"
          },
          {
            "name": "--config",
            "type": "named",
            "format": "filepath",
            "isRequired": true,
            "description": "Path to an ep-mcp config YAML listing your ExpertPacks"
          },
          {
            "name": "--transport",
            "type": "named",
            "value": "stdio"
          }
        ],
        "environmentVariables": [
          {
            "name": "GEMINI_API_KEY",
            "isSecret": true,
            "description": "Gemini API key for embeddings (or set AZURE_OPENAI_API_KEY when using Azure OpenAI)"
          },
          {
            "name": "AZURE_OPENAI_API_KEY",
            "isSecret": true,
            "description": "Azure OpenAI key, if the config uses the Azure embedding provider"
          }
        ]
      }
    ],
    "repository": {
      "url": "https://github.com/brianhearn/ep-mcp",
      "source": "github"
    },
    "websiteUrl": "https://expertpack.ai",
    "description": "Serve ExpertPack knowledge packs over MCP with hybrid search, graph traversal and provenance."
  }
}

Report an issue with this listing