MCP server that exposes a Tock documentary chatbot (RAG) as an MCP tool, allowing AI clients to query Tock and receive formatted responses with source document links.
Single tool 'ask_tock' has a clear verb-noun name and comprehensive schema with proper types and descriptions. Input schema includes 'question' (required, string) and 'headers' (optional, object with additionalProperties). Tool description is 127 chars, within the 10-1024 baseline. Parameter descriptions are present and actionable. Tool annotations (readOnlyHint, idempotentHint) are correctly declared. However, output schema is not documented, the tool returns Markdown text but the response structure is not formally specified. Error handling is basic (empty question check, generic Tock error wrapping) without recovery guidance or error categorization. No pagination, batching, or composition patterns evident.
Ask a question to the Tock documentary chatbot (RAG). Returns the text response and links to source documents.
Output schema not documented. Tool returns Markdown text but response structure (fields, types, format) is not formally specified. LLMs cannot plan downstream operations or extract structured data.
Error handling lacks recovery guidance. Generic 'Tock error: <err>' response does not tell the LLM what to do next (retry, ask user, abort). No error categorization (retryable vs fatal).
No timeout or rate-limit documentation. Tool calls external Tock API but no guidance on expected latency, timeout behavior, or rate limits. Agents may hang or retry excessively.
Headers parameter is optional but undocumented in terms of valid keys or expected behavior. Description says 'e.g. X-Toki-Filter, X-Toki-Origin' but does not specify which headers are actually supported or their effects.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | B | 70 | 2026-07-28+ | v2 |
No pagination or result-limiting pattern. If Tock returns large responses, no mechanism to cap or paginate results. Risk of context window exhaustion.