🧠 Recall — a local, private knowledge-base MCP server

Recall turns a folder of your own notes and documents into a searchable knowledge base that any AI assistant can use. It is a Model Context Protocol (MCP) server: connect it to Claude Desktop, Claude Code, or any MCP client, and the assistant can search, read, and add to your notes through well-defined tools.

It uses semantic search powered by local embeddings, so it finds passages by meaning, not just matching keywords — and it runs entirely on your machine. No API key, no cloud, your documents never leave your device.

Why this project is interesting

• Retrieval-Augmented Generation (RAG) done locally — chunking, embeddings, and cosine-similarity retrieval, the core of modern AI knowledge systems.

• Hybrid retrieval — fuses semantic and keyword results with Reciprocal Rank Fusion (RRF), the technique production search systems use.

• Model Context Protocol — exposes capabilities as tools an LLM can call, the emerging standard for connecting AI assistants to real systems.

• Privacy-first — semantic search runs on-device with a small embedding model; nothing is sent to a third party.

• Graceful degradation — if the embedding model can't load, it automatically falls back to keyword search instead of breaking.

Related MCP server: mcp-memory-search

See it in action

Ask Claude (with Recall connected) "search my notes for how to undo a git commit" — it calls the search_documents tool and answers grounded in git-cheatsheet.md, entirely on your machine.

See the difference: keyword vs. semantic

Ask "how do I undo a commit?" against a small dev knowledge base:

Semantic search finds the genuinely useful answer even though the words don't overlap. That is the whole point of embeddings.

Retrieval quality (measured)

A small labelled eval (10 paraphrased queries over the sample docs) compares the three search modes. Semantic beats keyword clearly, especially at recall@1:

Reproduce it with python eval/run_eval.py. The corpus is small and topically overlapping, so treat the numbers as illustrative. (Pure semantic edges out hybrid here; hybrid tends to win when exact keyword matches matter — codes, names, error strings.) The harness is the real point: retrieval quality is measured, not assumed.

What the AI can do (the MCP tools)

How it works

        Your documents (.md / .txt / .pdf)
                 │
                 ▼
        ┌───────────────────┐
        │  DocumentStore     │   1. split each file into paragraph "chunks"
        │  (recall/store.py) │   2. embed every chunk into a vector (local model)
        └───────────────────┘
                 │  query
                 ▼
        ┌───────────────────┐
        │  Semantic search   │   embed the query, rank chunks by cosine similarity
        │  (or keyword)      │   (falls back to keyword search if no model)
        └───────────────────┘
                 │  tools
                 ▼
        ┌───────────────────┐        MCP (stdio / JSON-RPC)
        │  FastMCP server    │ ◀───────────────────────────▶  Claude Desktop,
        │  (recall/server.py)│                                 Claude Code, ...
        └───────────────────┘

1. Chunk — documents (Markdown, plain text, or PDF) are split on blank lines into passages, with each Markdown heading kept attached to the text it introduces, so results land on a precise, self-contained passage.

2. Embed — each chunk is turned into a vector with a local fastembed model (bge-small-en-v1.5, 384-dimensional vectors).

3. Retrieve — a query is embedded and compared to every chunk by cosine similarity; the closest chunks win.

4. Serve — the FastMCP server exposes search/read/write as MCP tools over stdio, so any MCP client can use them.

Quickstart

Requires Python 3.10+.

# 1. Clone and enter the project
git clone https://github.com/jaswanthsurya007-source/recall-mcp.git
cd recall-mcp

# 2. Create and activate a virtual environment
python -m venv .venv
# Windows (PowerShell):
.venv\Scripts\Activate.ps1
# macOS / Linux:
source .venv/bin/activate

# 3. Install
pip install -e .

# 4. Try a search from Python
python -c "from recall.store import DocumentStore; s=DocumentStore('data/documents'); print([r.chunk.source for r in s.search('how do I undo a commit', 1)])"

The first run downloads the embedding model (~66 MB) once, then caches it.

Behind a corporate proxy?

Recall uses truststore to trust your operating system's certificates automatically, so it works on networks that inspect TLS traffic (common at large companies) without extra configuration.

Connect it to Claude Desktop

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

{
  "mcpServers": {
    "recall": {
      "command": "/absolute/path/to/recall-mcp/.venv/bin/python",
      "args": ["-m", "recall.server"],
      "env": {
        "RECALL_DOCS_DIR": "/absolute/path/to/recall-mcp/data/documents"
      }
    }
  }
}

On Windows, use the full path to python.exe and escape backslashes, e.g. "C:\\path\\to\\recall-mcp\\.venv\\Scripts\\python.exe".

Restart Claude Desktop, and you'll see Recall's tools available. Ask it things like "Search my notes for how to undo a git commit" or "Save a note titled 'Meeting' with these action items…".

Use your own documents

Point Recall at any folder of .md, .txt, or .pdf files:

# macOS / Linux: set RECALL_DOCS_DIR to your own notes folder
RECALL_DOCS_DIR="/path/to/my/notes" python -m recall.server

# Windows (PowerShell)
$env:RECALL_DOCS_DIR = "C:\path\to\my\notes"; python -m recall.server

The data/documents/ folder ships with a few sample notes so you can try it immediately.

Running the tests

pip install -e ".[dev]"
pytest -q

The test suite runs fully offline (keyword mode), so it needs no model download.

Project structure

recall-mcp/
├── .github/workflows/ # CI: ruff + pytest on every push
├── recall/
│   ├── server.py      # FastMCP server: defines the MCP tools
│   ├── store.py       # load → chunk → search (semantic, keyword, hybrid)
│   └── embeddings.py  # local embedding model wrapper (fastembed)
├── data/documents/    # sample knowledge base (.md and .pdf)
├── tests/             # offline pytest suite (+ fixtures/)
├── eval/              # retrieval-quality eval (recall@k)
├── pyproject.toml     # packaging + tooling config
├── requirements.txt
└── LICENSE

Design notes

• Why local embeddings? Privacy and zero cost. fastembed uses ONNX runtime rather than PyTorch, so installs are small and inference is fast on CPU.

• Why chunk by paragraph? It is simple and transparent, and it makes results land on a focused passage. A future version could use overlapping token windows.

• Why a fallback to keyword search? A tool should never hard-fail. If the model can't be downloaded, search still works — just less cleverly.

• Re-indexing on write is a full reload for clarity; at larger scale you would embed only the newly added chunks.

Roadmap

• Retrieval-quality eval harness (recall@k)

• Hybrid search (Reciprocal Rank Fusion of semantic + keyword)

• PDF document support

• Persist embeddings to disk so startup is instant on large corpora

• Support HTML documents

• Optional LLM-generated summaries via the Claude API

• Expose documents as MCP resources, not just tools

License

MIT