mcp-fts5-starter

Drop-in MCP server template with SQLite FTS5 search backend. ~300 lines, no vector DB, no embedding API, runs on a Pi.

The problem

You want to expose a corpus of notes, docs, or clippings to Claude (or any MCP client) as a search tool. Most tutorials reach for a vector DB, an embedding API, and a 500MB Docker image to retrieve a few thousand markdown files. For a small-to-medium corpus running on a single machine, that's overkill.

mcp-fts5-starter is the boring, dependable option:

• SQLite FTS5 for full-text search — built into Python's sqlite3, no service to run

• MCP server scaffold with a few example tools (search, list, read)

• One-file ingest script that walks a directory of markdown files, parses frontmatter, and indexes them

• No embeddings, no vectors, no GPU — and no API bill

Drop the template into a new repo, point it at a folder, and you have a working MCP server in under 10 minutes.

When to use this (and when not to)

Use this if your corpus is:

• Small-to-medium (up to ~100k documents)

• Mostly text (markdown, code, prose) where keyword + tag matching is enough

• Running on a single machine, Pi, or laptop

• Something you want to set up once and forget

Don't use this if you need:

• True semantic search across rephrased queries — pair this with embeddings, or use a different tool

• Multi-tenant search across millions of docs — use a real search backend (Elastic, Meilisearch, Qdrant)

• Memory decay / TTL on entries — see forget-rag (which also uses FTS5 but for a different purpose)

Sibling projects

Both use SQLite FTS5 under the hood, but solve different problems. Need a starter? Here. Need decay logic? Forget-rag.

Quick demo

The repo ships with a small synthetic corpus under data/sample/ and a one-shot script that builds an index and runs a few representative queries against it:

git clone https://github.com/zx22413/mcp-fts5-starter
cd mcp-fts5-starter
uv sync                          # or: pip install -e .
python scripts/build-sample.py

Sample output:

Rebuilding index at data/sample/index.db
  indexed 7 doc(s): 7 written, 0 failed

Query: 'BM25 weights'
  - BM25 ranking                concepts/bm25.md
  - Why not just use a vector   notes/why-not-vector-db.md

Query: 'hybrid search'
  - Reciprocal rank fusion      concepts/rrf.md
  - Why not just use a vector   notes/why-not-vector-db.md

Query: 'tokenizer' [doc_type=notes]
  - Tokenization trade-offs     notes/tokenization-tradeoffs.md
  - Why not just use a vector   notes/why-not-vector-db.md
  - Incremental indexing        notes/incremental-indexing.md

To launch the MCP server against the same corpus (e.g. for use from Claude Code), point at the directory and the index file:

MCP_FTS5_CORPUS=data/sample MCP_FTS5_DB=data/sample/index.db \
  mcp-fts5-starter serve

For a hosted deployment, swap stdio for sse or streamable-http:

mcp-fts5-starter serve --transport sse --host 0.0.0.0 --port 8765

Architecture & benchmarks

• docs/architecture.md — design pillars (FTS5-first, embeddings opt-in, generic schema/tools, incremental sync), what didn't survive extraction from the upstream project, and a comparison table for when BM25 / hybrid / hosted vector DB each makes sense.

• docs/benchmark.md — reproducible benchmark at 100 / 1,000 / 10,000 docs, plus the perf bug it surfaced.

Examples

• examples/claude-code/ — drop-in .mcp.json for Claude Code, plus how-to and troubleshooting. Same shape works for Claude Desktop.

• examples/raw-jsonrpc/ — talk to the server using bare JSON-RPC over stdio (no MCP SDK). Useful when writing a custom client or debugging a transport-level issue.

Status

✅ v0.2.0 shipped (PyPI · GitHub Release · launch post) — adds HTTP transports, a real benchmark, and ~2× faster ingest.

Roadmap to v0.1

• 1. Initial scaffold

• 2. Generic MCP tool layer (search, list, read, index)

• 3. Generic FTS5 schema with BM25 tuning notes

• 4. Sample corpus + one-command demo (scripts/build-sample.py)

• 5. Architecture doc — docs/architecture.md

• 6. examples/ — Claude Code config + raw JSON-RPC over stdio

• 7. CI workflows (test on push/PR × py3.11/3.12/3.13; publish on release via OIDC)

• 8. v0.1.0 release (PyPI) + launch post

License

MIT — see LICENSE.