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codicil — Documentation Indexing MCP

Repo documentation made queryable through MCP. codicil indexes your repository's Markdown, YAML, and TOML documentation into a local Chroma vector store. AI agents can then query docs semantically. Uses Ollama embeddings when available; gracefully degrades to keyword search with zero external dependencies.

What It Does for Operators

  • Semantic doc search — AI agents can query your project's documentation using natural language
  • Zero infra — No external API keys needed. Works entirely locally with Chroma (embedded) + optional Ollama
  • Graceful degradation — If Ollama isn't running, falls back to live keyword search on disk — no failure mode
  • Auto-reindex — Reindex command for when docs change
  • Multi-format — Supports Markdown, YAML, and TOML documentation files

Installation

git clone https://github.com/colehellman/codicil.git
cd codicil
pip install -r requirements.txt

# Optional: Install Ollama for semantic search
# curl -fsSL https://ollama.com/install.sh | sh
# ollama pull nomic-embed-text

Claude Desktop / Hermes Config

{
  "mcpServers": {
    "codicil": {
      "command": "python",
      "args": ["-m", "codicil.server"],
      "env": {
        "CODICIL_DOCS_PATH": "/path/to/your/repo/docs"
      }
    }
  }
}

Key Tools

Tool Description
query_docs Semantic (or keyword) search across indexed documentation
reindex_docs Rebuild the vector index from updated docs
get_doc_context Retrieve the full document for a search result
list_indexed List all indexed documents and their status

Note: Tool names are approximate. See github.com/colehellman/codicil.

Operator Use Cases

  1. Development Teams — "How do we handle error retries in our SDK?" → AI agent queries internal docs
  2. Technical Writers — Verify documentation coverage by querying for missing topics
  3. Onboarding — New team members ask AI agents about internal processes instead of digging through wikis
  4. Open Source Maintainers — Give contributors AI-powered documentation search

CorpusIQ Angle

codicil solves an internal documentation problem that many CorpusIQ operators face: "where is that documented?" For operators building on CorpusIQ, codicil can index the CorpusIQ docs alongside their own internal documentation, giving AI agents a unified knowledge surface.

Limitations

  • Chroma is embedded (not production-scale for massive doc corpuses)
  • Semantic search quality depends on Ollama embedding model
  • No cloud sync — each developer runs their own index
  • Python-only (no Node/TypeScript version)