# MCP-PersonalSearch

An MCP server designed to give the model access to the same data the operator has available during their normal interactive sessions.

Currently implemented: **Phase 1** of the project PRD — the local documentation pipeline (raw store → markdown extraction → chunking → FTS5 index → CLI) for GitLab-hosted docs-as-code repositories. The MCP server itself (Streamable HTTP, `search_docs`/`get_section`/etc.) is Phase 2 and not yet built.

## Setup

```bash
python -m venv .venv
.venv/Scripts/activate   # or `source .venv/bin/activate` on Linux/macOS
pip install -e ".[dev]"
```

Copy `config.example.toml` to `config.toml` and point `[[sources]]` at your repo(s):

```toml
[[sources]]
id = "eng-docs"
type = "gitlab_repo"
repo_url = "https://gitlab.example.com/team/docs.git"
branch = "main"
globs = ["docs/**/*.md", "README.md"]
```

## Usage

```bash
docsrag ingest --source eng-docs        # clone/fetch + index; safe to re-run, skips unchanged files
docsrag search "your question here"     # lexical (BM25) search over the indexed corpus
docsrag reindex                         # rebuild sections/chunks/FTS from the raw store, fully offline
docsrag status                          # per-source document counts and last run
docsrag eval --set eval/questions.json  # recall@k / MRR against a labelled question set (PRD §7.2)
```

`corpus.db` (the indexed documentation) and `instance.db` (query log, job history) are written under `data/` by default and are gitignored — see PRD §12 on why the corpus must never be shared, exported, or synced.

`eval/questions.json` is also gitignored, for the same reason: real questions are grounded in whatever you actually ingested and can embed internal content. Copy `eval/questions.example.json` to `eval/questions.json` and fill it in with `{"query": ..., "section_id": ...}` pairs from your own corpus (`section_id` values come from `docsrag search` output).

## Tests

```bash
pytest
```