Agent Pattern MCP

MCP server that provides AI agent pattern expertise for designing agent systems

What does this entry provide?

Agent Pattern MCP is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 76/100. 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 Agent Pattern 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: 76/100
Install method: oci
Repo: olk/agent-pattern-mcp
Repo updated: Oct 9, 2026

Install command

docker run -i --rm docker.io/olkowa/agent-pattern-mcp:1.0.4

Install method: oci

Review the upstream source before running this command.

GitHub project

Quality

Repo stars

Repo updated

Oct 9, 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": "2026-10-09T23:20:45.310433Z",
      "publishedAt": "2026-10-09T23:20:45.310433Z",
      "statusChangedAt": "2026-10-09T23:20:45.310433Z"
    }
  },
  "server": {
    "name": "io.github.olk/agent-pattern-mcp",
    "title": "Agent Pattern MCP",
    "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
    "version": "1.0.4",
    "packages": [
      {
        "transport": {
          "type": "stdio"
        },
        "identifier": "docker.io/olkowa/agent-pattern-mcp:1.0.4",
        "runtimeHint": "docker",
        "registryType": "oci",
        "packageArguments": [
          {
            "type": "positional",
            "value": "--transport"
          },
          {
            "type": "positional",
            "value": "stdio"
          }
        ],
        "runtimeArguments": [
          {
            "name": "--add-host",
            "type": "named",
            "value": "host.docker.internal:host-gateway",
            "description": "Makes a TEI embedder/reranker running on the Docker host reachable from the container (needed on Linux; already provided by Docker Desktop)."
          }
        ],
        "environmentVariables": [
          {
            "name": "GENERATOR_API_KEY",
            "format": "string",
            "isSecret": true,
            "isRequired": true,
            "description": "API key for the generator LLM (LiteLLM provider)."
          },
          {
            "name": "GENERATOR_PROVIDER",
            "format": "string",
            "default": "openai",
            "description": "LiteLLM provider prefix, e.g. openai, anthropic, openrouter, minimax."
          },
          {
            "name": "GENERATOR_MODEL",
            "format": "string",
            "default": "gpt-4o-mini",
            "description": "Model name, combined as <provider>/<model> via LiteLLM."
          },
          {
            "name": "GENERATOR_BASE_URL",
            "format": "string",
            "description": "OpenAI-compatible base URL (LiteLLM api_base)."
          },
          {
            "name": "PATTERN_DIRECTORY",
            "format": "string",
            "default": "/app/pattern",
            "description": "Directory with *-pattern.json files. The image bakes the 61-pattern catalog at /app/pattern."
          },
          {
            "name": "EMBEDDER_PROVIDER",
            "format": "string",
            "default": "tei",
            "description": "Embedding backend for pattern retrieval: tei, openai, ollama, vllm or hosted_vllm."
          },
          {
            "name": "EMBEDDER_BASE_URL",
            "format": "string",
            "description": "Embedder endpoint. For TEI sidecars on the host use http://host.docker.internal:8080/v1 (add --add-host=host.docker.internal:host-gateway on Linux)."
          },
          {
            "name": "EMBEDDER_API_KEY",
            "format": "string",
            "default": "not-needed",
            "isSecret": true,
            "description": "API key for the embedder. Required for startup even with keyless TEI; use 'not-needed' there."
          },
          {
            "name": "RERANKER_BASE_URL",
            "format": "string",
            "description": "TEI cross-encoder reranker endpoint used during pattern retrieval."
          }
        ]
      }
    ],
    "repository": {
      "url": "https://github.com/olk/agent-pattern-mcp",
      "source": "github"
    },
    "description": "MCP server that provides AI agent pattern expertise for designing agent systems"
  }
}

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