MCP server for querying Ragdoll AI knowledge bases
Single tool 'query-knowledge-base' has critical definition gaps. Tool description is completely missing (null). All three parameters lack descriptions (null values). Input schema is present with Zod validation (query: string, topK: number 1-10 optional, rerank: boolean optional), but parameter descriptions are absent. Output schema is undocumented, response structure inferred from code (array of objects with content, similarity, score, metadata fields). No error handling guidance. Naming is acceptable (verb_noun pattern) but insufficient to overcome missing descriptions.
Tool description is null/missing. LLMs cannot determine when or why to select this tool without a description explaining its purpose, use cases, and what it returns.
All three input parameters lack descriptions (null values). Parameter 'query', 'topK', and 'rerank' have no text explaining what they control, expected format, or constraints. LLMs cannot infer parameter semantics from names alone.
Output schema is undocumented. Response structure (array of objects with content, similarity, score, metadata) is inferred from code, not declared. LLMs need explicit documentation of return fields to plan downstream operations.
No error handling guidance. Tool calls external API (ragdollai.io) but provides no recovery hints if the API fails, knowledge base ID is invalid, or authentication fails. LLM has no guidance on what to do next.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 32 | 2026-07-28+ | v2 |
API credentials (RAGDOLL_KNOWLEDGE_BASE_ID, RAGDOLL_API_KEY) are correctly injected via environment variables, but no documentation explains this to users. Tool description should note that setup requires these env vars.