docsonar

<!-- mcp-name: io.github.ribhav-jain/docsonar -->

Chat with your folders. A local document search MCP server — your files never leave your machine.

docsonar indexes folders of documents into a single SQLite database and gives any MCP client (Claude Desktop, Claude Code, and others) hybrid keyword + semantic search over them. Fully offline, zero configuration, read-only by design.

Quickstart

Claude Code

Add to your project's .mcp.json (or ~/.claude.json for all projects):

{
  "mcpServers": {
    "docsonar": {
      "command": "uvx",
      "args": ["docsonar"]
    }
  }
}

Claude Desktop

Add to claude_desktop_config.json (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "docsonar": {
      "command": "uvx",
      "args": ["docsonar"]
    }
  }
}

Running from a source checkout instead: "command": "uv", "args": ["run", "--directory", "/path/to/docsonar", "docsonar"].

Then just talk to it: "Add my ~/Documents/notes folder and find everything about quarterly planning." The first index downloads the embedding model (~130 MB, one time); keyword search works immediately while that happens.

For HTTP instead of stdio: uvx docsonar --transport http --port 8365.

Tools

Tool	Purpose
add_folder(path, include_globs?, exclude_globs?)	Register a folder; indexing starts in the background
remove_folder(path)	Unregister and purge its index data
list_folders()	Registered folders with counts and last index time
search(query, top_k?, folder?, file_type?, mode?)	Hybrid keyword+semantic search (RRF-fused); ranked passages with file path, heading/page location, and snippet
read_file(path, start_line?, end_line?)	File text (extracted text for pdf/docx/html); refuses paths outside registered folders
find_similar(path, top_k?)	Documents most similar in meaning to a given file
reindex(folder?, force?)	Incremental refresh: new/changed files reindexed, deleted files purged
index_status()	Index totals, embedding model state, background job progress, failed files

Supported formats: txt, md, pdf (page-aware, cites p. 12), docx, html.

Architecture

flowchart LR
    Client["MCP client<br/>(Claude Desktop / Code)"] <-->|stdio or HTTP| Server["FastMCP server<br/>8 tools"]
    Server --> Sec["path security<br/>(registered folders only)"]
    Server --> Search["hybrid search<br/>BM25 + cosine KNN + RRF"]
    Server --> Worker["background indexer<br/>(worker thread + queue)"]
    Worker --> Parsers["parsers<br/>txt · md · pdf · docx · html"]
    Parsers --> Chunker["heading/page-aware chunker<br/>~400 tokens, 60 overlap"]
    Chunker --> DB[("SQLite<br/>FTS5 + sqlite-vec")]
    Search --> DB
    Embed["sentence-transformers<br/>bge-small-en-v1.5 (local)"] --> Worker
    Embed --> Search

Every chunk stores its location (heading path like Setup > Windows, or PDF page range), so search results can cite report.pdf, p. 12.

Design decisions

• Read-only by design. There are no write, move, or delete tools, and there never will be. The server only reads files inside folders you explicitly register — with symlink-escape protection and strict path resolution — so the blast radius of a misbehaving client is zero.
• SQLite as the single store. FTS5 gives production-grade BM25 keyword search in the standard library, and sqlite-vec puts vectors in the same file. One database file, no services to run, trivial to back up or delete.
• Hybrid search by default. BM25 and cosine-KNN rankings are fused with reciprocal-rank fusion (k=60). Exact identifiers and rare terms win on the keyword side; paraphrased questions win on the semantic side; RRF needs no score calibration between them. mode lets the caller force either side.
• Local embeddings. BAAI/bge-small-en-v1.5 (384-dim) — same size class as the classic all-MiniLM-L6-v2 but stronger on retrieval benchmarks. Downloads on first index, runs on CPU, lazy-loaded so server startup stays instant. If the model can't load, everything degrades gracefully to keyword search and tool responses say so.
• A tool surface built for an LLM caller. search returns enough per hit (path, location, score, snippet) to decide what to read next without another round trip; results carry stable chunk_ids; every degradation is reported in-band via note/embedding_note fields instead of failing. There's no "answer the question" tool on purpose — the calling model does the reasoning; docsonar does retrieval.
• Incremental by content, not just mtime. Reindexing checks mtime+size first, then falls back to a SHA-256 content hash — touched-but-identical files are skipped, and deleted files are purged from both the FTS and vector indexes.
• Scanned PDFs fail loudly. A PDF with no extractable text is reported as failed with a clear reason rather than silently indexed as empty. OCR is out of scope.

Benchmarks

Synthetic corpus of 200 markdown files (600 chunks); Intel Core Ultra 5 125U (laptop, CPU-only), Windows 11, Python 3.12. Reproduce with uv run python scripts/benchmark.py.

Operation	Result
Index, keyword-only	0.7 s (≈280 files/s)
Index, with embeddings	59 s (≈3.4 files/s — embedding-bound)
Incremental reindex, nothing changed	0.03 s
Search, keyword	8.9 ms median
Search, semantic	35 ms median
Search, hybrid	54 ms median
Embedding model load (once per process)	~21 s
Database size	2.6 MB (1.1 MB keyword-only)

Configuration

Zero config needed. To customize, create config.toml in the platform config directory (Windows: %LOCALAPPDATA%\docsonar\, macOS: ~/Library/Application Support/docsonar/, Linux: ~/.config/docsonar/):

embedding_model = "BAAI/bge-small-en-v1.5"  # any sentence-transformers model
chunk_target_tokens = 400
chunk_max_tokens = 512
chunk_min_tokens = 100
chunk_overlap_tokens = 60
max_file_size_mb = 50
extra_ignore_dirs = ["Archive"]

CLI flags: --transport stdio|http, --host, --port, --db-path, --config.

The index database lives in the platform data directory (Windows: %LOCALAPPDATA%\docsonar\, macOS: ~/Library/Application Support/docsonar/, Linux: ~/.local/share/docsonar/).

Running from source

Requires uv (it installs the right Python automatically):

# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

git clone https://github.com/ribhav-jain/docsonar.git
cd docsonar
uv sync --dev    # creates .venv and installs everything, incl. dev tools

Run the tests

uv run pytest                          # full suite (~2 s, no model download needed)
uv run pytest -v                       # verbose, one line per test
uv run pytest tests/test_search.py     # one file
uv run pytest -k incremental           # tests matching a keyword
uv run ruff check .                    # lint
uv run mypy src tests                  # strict typecheck
uv run python scripts/benchmark.py     # perf numbers (downloads the real model)

Run the server

uv run docsonar                                  # stdio — for MCP clients (Claude Desktop/Code)
uv run docsonar --transport http --port 8365     # HTTP — for manual testing (Postman, curl)

VS Code: press F5 — launch configs for the HTTP server and the test suite are in .vscode/launch.json.

Try the tools without an MCP client

With the HTTP server running, the endpoint is http://127.0.0.1:8365/mcp. Recent Postman versions can connect directly (New → MCP Request, transport HTTP) and show all 8 tools as forms. For raw HTTP, MCP is JSON-RPC over POST with two setup calls, then tool calls. Send every request with headers Content-Type: application/json and Accept: application/json, text/event-stream:

// 1. initialize — copy the `mcp-session-id` RESPONSE header and send it
//    back as a request header on every call below
{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"postman","version":"1.0"}}}

// 2. initialized notification (expect 202, empty body)
{"jsonrpc":"2.0","method":"notifications/initialized"}

// 3. list the tools
{"jsonrpc":"2.0","id":2,"method":"tools/list"}

Then call tools — the repo ships a sample corpus in examples/sample-docs to play with (use forward slashes in JSON to avoid escaping; they work on Windows too):

// Register the sample folder (first ever call downloads the embedding model, ~30 s)
{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"add_folder",
  "arguments":{"path":"C:/path/to/docsonar/examples/sample-docs"}}}

// Watch indexing progress until indexing.active is false
{"jsonrpc":"2.0","id":4,"method":"tools/call","params":{"name":"index_status","arguments":{}}}

// Hybrid search — finds the expense-policy PDF, cites its page
{"jsonrpc":"2.0","id":5,"method":"tools/call","params":{"name":"search",
  "arguments":{"query":"meal reimbursement limit","top_k":3,"mode":"hybrid"}}}

// Semantic search — no keyword overlap needed
{"jsonrpc":"2.0","id":6,"method":"tools/call","params":{"name":"search",
  "arguments":{"query":"how hot should water be for green tea","mode":"semantic"}}}

// Read a hit (works on PDFs too — returns extracted text)
{"jsonrpc":"2.0","id":7,"method":"tools/call","params":{"name":"read_file",
  "arguments":{"path":"C:/path/to/docsonar/examples/sample-docs/expense-policy.pdf"}}}

// "More like this"
{"jsonrpc":"2.0","id":8,"method":"tools/call","params":{"name":"find_similar",
  "arguments":{"path":"C:/path/to/docsonar/examples/sample-docs/espresso-guide.md","top_k":3}}}

// Refresh after files change on disk (force:true rebuilds everything)
{"jsonrpc":"2.0","id":9,"method":"tools/call","params":{"name":"reindex","arguments":{}}}

// Clean up — unregisters the folder and purges its index data
{"jsonrpc":"2.0","id":10,"method":"tools/call","params":{"name":"remove_folder",
  "arguments":{"path":"C:/path/to/docsonar/examples/sample-docs"}}}

Security check worth trying: read_file with any path outside a registered folder (e.g. C:/Windows/System32/drivers/etc/hosts) returns a refusal, not file content.

CI runs lint, typecheck, and the test suite on Linux, macOS, and Windows.

License

MIT