DocDex - Documentation MCP Server

An MCP (Model Context Protocol) server that turns a directory of markdown documentation (wikis, runbooks, policy docs) into tools an AI assistant can search and cite. Point it at a docs folder, and Claude (or any MCP client) can find, read, and reference specific sections instead of guessing.

Tools exposed

The server's instructions tell the model to cite the ref of any section it relies on, so retrieval stays auditable instead of just trusting the model.

Related MCP server: Documentation MCP Server

Design decisions

• Heading-based chunking. Docs are split at markdown headings, and each chunk carries its full heading path (Runbook > Rollback procedure). Refs are stable (path#heading-path), so a citation today still resolves tomorrow if the doc hasn't changed.

• TF-IDF keyword ranking, no embeddings. This is deliberate: zero external services, zero API keys, runs anywhere, and the results are inspectable. You can see exactly why a chunk ranked. Heading matches get a 1.5x boost because headings carry dense signal. The KnowledgeIndex class is kept separate from the MCP wiring specifically so the ranker can be swapped for embeddings later without touching the server.

• Search-then-fetch, not dump-everything. search_docs returns short excerpts, and the model calls get_section only for what it needs. Keeps context windows small even on large corpora.

Quick start

pip install mcp
PYTHONPATH=src python -m docdex.server --docs ./sample_docs

Claude Desktop config

{
  "mcpServers": {
    "docdex": {
      "command": "python",
      "args": ["-m", "docdex.server", "--docs", "/path/to/your/docs"],
      "env": { "PYTHONPATH": "/path/to/docdex-mcp/src" }
    }
  }
}

Then ask Claude things like "what's our rollback procedure?" or "how long do we keep debug logs?" and it will search, fetch the section, and cite the ref.

Tests

python tests/test_index.py

Covers relevant-section ranking, heading-boost ordering, ref roundtrips, unknown-ref handling, corpus listing, and empty/stopword-only queries.

Layout

src/docdex/index.py    KnowledgeIndex: chunking, TF-IDF search, refs (no MCP dependency)
src/docdex/server.py   MCP wiring: 3 tools over the index
sample_docs/           small policy + runbook corpus to try it on
tests/test_index.py    index test suite (no pytest dependency)

Extension ideas

• Pluggable embedding ranker (the index/server split exists for this)

• File watcher for live reindexing on doc edits

• Confluence / Notion loaders alongside the markdown loader

• Per-source access scoping for multi-team corpora