Analytics Brain

Start here. Lists every knowledge-base document this user may read - webinar write-ups, newsletter issues, procedures and guides - as a flat array with `parentId`, so the tree can be reconstructed. Returns titles and slugs only; call read_document for a body. Some entries have `type: "FOLDER"` - they only group other entries and have no content of their own; do not try to summarise one, read its children instead. Each entry carries a short `description` - use those, plus the tree shape, to decide what is worth reading, then call search_documents for a question or read_document for a slug you already have. Never answer from a name or description alone. The list is already scoped to the user's access, so anything absent from it is not readable and should not be guessed at.

list_documents

Returns one document in full, as markdown, together with its ancestor chain for context. Takes the `slug` from list_documents or search_documents. If `type` comes back as "FOLDER", there is no body - it only groups other entries; use list_documents and filter by `parentId` to see what it contains. Cite the document by name when you use its content, so the reader can find the source. A slug the user has no access to comes back as an error, not as empty content - do not retry it.

read_document

Full-text search across the titles AND bodies of the knowledge-base documents this user may read. Returns matches with a snippet around the hit; call read_document for the full text. Prefer this over list_documents when the question is about what the documents say rather than what exists. Matching is a plain substring match, so search for distinctive words rather than whole questions - two or three narrow searches beat one long one.

search_documents