io.github.RuloGB/compendio-mcp

Your project's documentation, served to any agent in the fewest possible tokens.
A local RAG retrieval layer exposed as an MCP server. Your agent stops grepping and dumping whole files — it reaches the right paragraph instead.

What it does • Requirements • Quick Start • Configuration • MCP Tools • CLI • How it works • Incremental sync • Multilingual • Full docs

The problem

Your agent doesn't know your documentation. So it does what it can: grep, then cat a 400-line file to answer a question that lived in one paragraph. Three files later the context window is full of noise and the answer is still a guess.

Attaching the whole docs/ folder doesn't fix it — it just moves the waste earlier. Neither does keyword search: nobody writes questions using the exact words the document uses.

_{Same question, same model, and both answers are correct — the difference is what it cost to get there.
6 tool calls • 29s • 32,301 tokens  →  2 tool calls • 12s • 17,684 tokens.
Figures read from OpenCode's own elapsed label and cost tooltip, on an 81-document corpus.}

What Compendio does

Compendio indexes your markdown documentation and gives any AI agent three tools to find and read exactly what it needs.

• 🔍 Hybrid retrieval, not grep — keyword search finds the exact term, semantic search finds the paraphrase. Compendio runs both and merges the results.
• ✂️ Token-frugal by design — orient for ~10 tokens per document, search for a handful of fragments, read a single section. Never the whole corpus.
• 🔒 100% local — one SQLite file, embeddings on CPU, zero network calls at query time. No API keys, no Docker, no services, nothing leaves your machine.
• ♻️ Stays current — a running server picks up your documentation edits on its own. No watcher process, no manual rebuild loop.
• 🗣️ Multilingual — index documentation in any language. The embeddings model is multilingual and search is diacritic-insensitive. See [Multilingual](#mul

... View full README on GitHub