CCI Black Book MCP

An MCP server that does semantic search over the CCI Black Book — a large, scanned grow manual — and returns bounded, cited evidence packs for an MCP client (Claude Code, Codex, …) to answer from.

Scanned books are mostly pictures. Naive PDF-RAG indexes only the OCR text layer and loses every chart, diagram, and photo. This server embeds both the text and a render of each page, so the visual content is first-class in retrieval.

Requirements

• A Voyage AI API key — the embeddings are the whole point, so this is required.

• The source PDF (the CCI Black Book, or another scanned/image-heavy PDF).

• Docker, or Python 3.12 + uv.

Related MCP server: AXYS MCP Lite

How retrieval works

Three rankers, fused with weighted Reciprocal Rank Fusion:

• text-dense — voyage-context-4 contextualized chunk embeddings over the OCR text (one vector per chunk).

• image-dense — voyage-multimodal-3.5 over a PyMuPDF render of each page (one vector per page), so figures/photos/charts are retrievable even where the OCR layer is empty.

• FTS/BM25 — exact keyword matches over the OCR text.

Every page is rendered; a conservative, logged blank-page filter drops lined "Notes:" template pages (text-poor and ink-poor and color-poor) while keeping figure pages. Results carry unit_type = text or image; image citations use ids like p0042-img. Ingest fails loud if Voyage is unreachable (the prior index is left intact); queries degrade to FTS-only if a dense space is unavailable.

Quickstart

git clone https://github.com/dephekt/cci-blackbook-mcp
cd cci-blackbook-mcp

# 1. Secrets: a bearer token clients present, plus your Voyage key.
cp secrets/cci.env.example secrets/cci.env
#    then edit secrets/cci.env:
#      VOYAGE_API_KEY=...                     (from https://www.voyageai.com/)
#      CCI_VOYAGE_RETENTION_CONFIRMED=true    (after opting out — see Privacy)

# 2. Drop the PDF in.
mkdir -p data/source && cp "/path/to/CCI Black Book.pdf" data/source/document.pdf

# 3. Build, start, and index (~$0.60 one-time, ~8 min for a ~500-page book).
docker compose up -d --build
docker compose exec cci-blackbook cci-blackbook-ingest --force

docker compose up publishes the MCP on 127.0.0.1:8000 and serves the PDF/index from ./data — no reverse proxy or external network required. Point your client at http://127.0.0.1:8000/mcp (see Clients).

Verify Voyage connectivity first with cci-blackbook-ingest --smoke — it sends only synthetic data, so it's safe and essentially free.

Privacy

Ingest sends the book's OCR text and page images to Voyage's API. Voyage retains and may train on submitted data by default — turn on the one-way zero-retention opt-out in the Voyage dashboard, then set CCI_VOYAGE_RETENTION_CONFIRMED=true. Ingest is hard-gated on this flag and refuses to send anything otherwise.

Clients

Claude Code:

claude mcp add --transport http cci-blackbook http://127.0.0.1:8000/mcp \
  --header "Authorization: Bearer $CCI_BLACKBOOK_MCP_TOKEN"

Codex:

[mcp_servers.cci_blackbook]
url = "http://127.0.0.1:8000/mcp"
bearer_token_env_var = "CCI_BLACKBOOK_MCP_TOKEN"
tool_timeout_sec = 120

Tools

• ask_blackbook(question, crop_context=None, facility_context=None, max_citations=6)

• blackbook_search(query, limit=10, mode="hybrid") — modes: hybrid | vector | fts | text | image

• blackbook_read_citation(chunk_id) — accepts a text id (p0042-c001) or a page-image id (p0042-img)

• blackbook_status()

Configuration

Env-driven; the compose file sets sensible defaults. The ones you'll actually set:

Deploy behind a reverse proxy

deploy/pangolin.yml is an optional overlay (Pangolin/Newt shown; adapt for any proxy). It drops the published host port and fronts the container's :8000:

DOMAIN=example.com CCI_DATA_DIR=/srv/cci-blackbook \
  docker compose -f docker-compose.yml -f deploy/pangolin.yml up -d --build

Development

make test   # offline unit tests — deterministic fixtures, no network or API key
make lint   # ruff