Documentation search

appwrite_search_docs runs semantic search over the Appwrite documentation entirely in-process (replacing the standalone docs MCP server). It needs no project_id. Each query is embedded with OpenAI's text-embedding-3-small model, matched against a prebuilt index by cosine similarity, and the top-ranked pages are returned with their full content.

The index is a small committed artifact under src/mcp_server_appwrite/data/ (docs_index.npz + docs_index_meta.json), shipped in the image. The tool registers only when both the artifact and OPENAI_API_KEY are available; otherwise the server boots without it.

Runtime configuration

Variable	Required	Default	Purpose
OPENAI_API_KEY	Yes	—	Embeds each incoming query (one OpenAI call per search).
DOCS_SEARCH_MIN_SCORE	No	0.25	Minimum cosine score for a match.
DOCS_SEARCH_LIMIT	No	5 (max 10)	Default max pages returned.

Rebuilding the index

Re-run when the docs change, then commit the refreshed artifact:

OPENAI_API_KEY=sk-... uv run python scripts/build_docs_index.py

The script pulls the docs from the appwrite/website repo, chunks each page, embeds the chunks, and writes the artifact to data/. Optional build env vars:

Variable	Default	Purpose
DOCS_WEBSITE_REF	main	Git ref to pull docs from.
DOCS_EMBED_BATCH	100	Embedding batch size.