context-server

Semantic search over a folder of markdown, served as an MCP server for coding agents.

Index once into a SQLite DB (embeddings + BM25). Point Claude Code, Cursor, or any MCP client at serve, and the agent can search that corpus instead of guessing from memory.

One Rust binary. ONNX Runtime is linked in via ort / fastembed — no separate libonnxruntime to ship. SQLite is bundled.

Quick start

pip install context-server
# or: uvx context-server@latest …

context-server index --input ./docs --db context.db
context-server search --db context.db "how do we handle backports"
context-server serve --db context.db

Wheels: Linux x86_64/aarch64 (manylinux_2_39 / glibc 2.39+, e.g. Ubuntu 24.04+) and macOS Apple Silicon.

The first embedding run downloads All-MiniLM-L6-v2 into the local Hugging Face / fastembed cache (once, tens of MB).

Optional: tell the agent when to use this corpus

context-server index --input ./docs --db context.db \
  --instructions-file ./mcp-instructions.txt
# or: --instructions 'Use semantic_search for questions about …'

That text is stored in the DB and exposed as MCP ServerInfo.instructions when you serve.

Claude Code

claude mcp add --transport stdio --scope user context-server \
  -- uvx --refresh context-server@latest \
  serve --db /absolute/path/to/context.db

--refresh + @latest rechecks PyPI on each start. If Claude rarely surfaces the tools, set "alwaysLoad": true on the server entry in your Claude MCP config.

Cursor

~/.cursor/mcp.json (or project .cursor/mcp.json):

{
  "mcpServers": {
    "context-server": {
      "command": "uvx",
      "args": [
        "--refresh",
        "context-server@latest",
        "serve",
        "--db",
        "/absolute/path/to/context.db"
      ]
    }
  }
}

Reload MCP after editing. Re-index when content changes, then restart the MCP session so serve reloads the DB.

What it indexes

Only .md / .markdown. Chunks on # / ## / ###, keeps the heading path on each chunk, and splits long sections with overlap.

Convert structured sources (YAML, etc.) to prose before indexing. Fenced YAML searches poorly; a short paragraph that keeps names, roles, and relationships together works much better.

Try the sample set:

cargo build --release
./target/release/context-server index --input examples/sample-docs --dry-run
./target/release/context-server index --input examples/sample-docs --db /tmp/sample.db
./target/release/context-server search --db /tmp/sample.db "password reset"

Search

Default mode is hybrid: dense cosine (MiniLM) plus BM25, fused with reciprocal rank fusion. Dense catches paraphrase; BM25 catches exact tokens (usernames, acronyms, IDs).

context-server search --db context.db --mode hybrid "query"   # default
context-server search --db context.db --mode dense "query"
context-server search --db context.db --mode lexical "query"

# Scope to a subtree / heading / metadata tag
context-server search --db context.db --path-prefix teams/ "who owns storage"
context-server search --db context.db --heading Backport "z-stream"
context-server get --db context.db --path teams/storage.md --chunk 0

MCP tools

Tool	Role
semantic_search	Ranked passages + scores; optional path_prefix / heading / tag filters
list_documents	Indexed chunks; optional path_prefix
answer_question	Best matching passage(s) — retrieval only; same filters as search
get_document	Full chunk by citation (source_path + chunk_index), or all chunks for a path

Search hits cite chunks as source_path#chunk_index. Call get_document to pull the full text for quoting.

Remote database (GCS)

serve and search accept a gs:// URI. The object is cached under $XDG_CACHE_HOME/context-server/dbs/ (or ~/.cache/...). index still writes a local path only.

context-server serve --db 'gs://my-bucket/latest/context.db'

# Project-qualified form also works (gs:// required; stripped for the Storage API)
context-server serve --db \
  'gs://projects/my-gcp-project/buckets/my-bucket/objects/latest/context.db'

Uses Application Default Credentials. If a sibling {object}.sha256 exists (sha256sum format), a matching local cache is reused; otherwise the DB is re-fetched and verified.

CLI

context-server index  --input <path> [--db FILE] [--dry-run] [--batch N]
                      [--instructions TEXT | --instructions-file FILE]
context-server serve  --db <local path | gs://…>
context-server search --db <local path | gs://…> [--limit N] [--mode hybrid|dense|lexical]
                      [--path-prefix P] [--heading H] [--tag T] <query>
context-server get    --db <local path | gs://…> --path FILE [--chunk N]
context-server embed  <text>          # smoke-test embeddings

Build from source

cargo build --release
cargo test

Rust 1.75+, Linux x86_64 is the primary target. You need a C++ stdlib for the linker (libstdc++) and whatever OpenSSL/native-tls needs on your platform.

On Fedora/RHEL, if the linker wants -lstdc++ but only libstdc++.so.6 exists:

mkdir -p .linker && ln -sfn /usr/lib64/libstdc++.so.6 .linker/libstdc++.so
export RUSTFLAGS="-L native=$(pwd)/.linker"

Linux wheels (same image CI uses — Ubuntu 24.04 / glibc 2.39):

./scripts/build-wheel.sh
VERSION=2026.716.1 ./scripts/build-wheel.sh   # optional override

Releasing

CalVer YYYY.MMDD.N (e.g. 2026.716.1) so versions work for both Cargo and PyPI. Run the Release workflow on main (Actions UI or CLI); it picks the next version, builds wheels, publishes to PyPI, then creates the matching git tag.

gh workflow run release.yml --repo context-server/context-server

Design notes

Under the hood: fastembed All-MiniLM-L6-v2 (384-d, L2-normalized), rusqlite with float32 blobs, rmcp over stdio. Each index run replaces the DB contents.

More detail and roadmap: PLAN.md.

License

MIT — see LICENSE.