MCP Tools Reference
Knowledge Raven provides 6 MCP tools. Your AI agent automatically selects the right tool based on your request — you don’t need to call them manually.
How the Agent Uses These Tools
Knowledge Raven uses Agentic RAG with 3 query types. The agent selects the optimal retrieval strategy automatically:
| Query Type | Best For | Strategy |
|---|---|---|
| Precision | Specific questions — “What is our vacation policy?” | Vector-heavy hybrid search |
| Explorative | Broad topics — “What do we know about the onboarding process?” | BM25-heavy hybrid search |
| Discovery | Browsing — “What documents are available about the Q3 project?” | Metadata-only, no vector search |
search_knowledge_base
Precision search across one or more knowledge bases.
Use when you have a specific question. Can search within a specific knowledge base or across multiple knowledge bases in a workspace. Returns the most relevant chunks with parent context.
Example user prompts:
- “Search my knowledge base for our customer refund policy”
- “Find the API authentication documentation in the Engineering KB”
Returns: Ranked chunks with parent context, source links, and relevance scores.
broad_search
Explorative search across all knowledge bases.
Use when the question is broad, exploratory, or could span multiple knowledge bases. Uses BM25-heavy search to find keyword matches across all available content.
Example user prompts:
- “What does our company know about GDPR compliance?”
- “Find everything related to our Q3 OKRs”
Returns: Results from all knowledge bases the user has access to, ranked by relevance.
list_documents
Browse documents in a knowledge base with summaries.
Returns documents with auto-generated 2–3 sentence summaries. Useful for discovery without performing a search — the third query type alongside precision and explorative search.
Example user prompts:
- “What documents are in the HR knowledge base?”
- “List all documents about the product roadmap”
Returns: Document list with summaries, file types, and source links.
fetch_document
Retrieve a specific document — preview, full content, or by chunks.
The agent uses this when search results provide insufficient context and it needs to see more of a document’s content. Three modes:
- preview — First ~500 words + document summary. Use to check if a document is relevant before fetching the full content.
- full — Complete document text. Use when full context is needed.
- chunks — Specific chunks by ID. Use for targeted retrieval after search.
Example user prompts:
- “Show me the full text of the onboarding guide”
- “Get a preview of the Q3 report”
Returns: Document content with source link for deep-linking back to the original.
list_knowledge_bases
Discover available knowledge bases.
Returns all knowledge bases the current user has access to, with names, descriptions, and document counts. The agent uses this to determine which knowledge bases are available and to use their IDs as parameters in searches — for example to narrow a search to a specific knowledge base or exclude irrelevant ones when the user refers to a particular source.
Example user prompts:
- “What knowledge bases do I have access to?”
- “Search only the Engineering knowledge base for API docs”
Returns: Knowledge base list with metadata (name, description, document count, connector types).
get_document_metadata
Retrieve metadata without loading full content.
Returns title, source URL, file type, last synced date, and connector type — without the document body. Useful when the agent needs to verify a document exists or get its source link.
Example user prompts:
- “When was the employee handbook last updated?”
- “Where is the original source for document X?”
Returns: Metadata including source_link for deep-linking to the original document.
Source Deep-Linking
All search and fetch tools return a source_link field. When you ask your agent about a topic, it can provide clickable citations that open the original document at the cited passage — directly in Confluence, Notion, GitHub, or your Knowledge Raven dashboard.