MongoDB MCP Server providing access to novel data with optimizations for small context windows
Server has 5 tools with basic definitions but significant quality gaps. Tool names follow verb_noun convention (query, get_*, search_*, update_*), which is good. However, descriptions are minimal (10-50 chars), parameter descriptions are sparse or missing, and input schemas lack proper type definitions and constraints. The update_chapter_summary tool exposes an auth_token parameter, violating secret-injection patterns. No output schemas are documented. Error handling is absent from visible code. Only 1 of 5 tools has reasonably complete parameter documentation.
Retrieve specific chapter content by ID
Retrieve specific character details by ID
Execute a natural language query against the database
Search database using regex patterns
Update chapter summary with authentication
Auth token exposed as tool parameter (update_chapter_summary). Credentials must never appear in parameters, use server-side secret injection via environment variables or vault. Agent traces log all parameters, leaking secrets into logs and prompt history.
Input schemas lack proper type definitions. Parameters are declared as strings with minimal constraints. No enums for constrained values, no min/max for numeric ranges, no format specifications. This invites hallucinated or invalid input from LLMs.
Parameter descriptions are missing or trivial. 'The natural language query to execute' and 'The regex pattern to search for' lack context on format, constraints, or when to use each tool. LLMs cannot infer parameter meaning from names alone.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 46 | <=2025-11-25 | v2 |
No output schemas documented. LLMs cannot plan downstream tool calls or extract required fields without knowing what each tool returns. Responses appear to be formatted text (via formatting module) but structure is not declared.
No error handling guidance. Code shows no recovery hints, error classification, or actionable messages. If a query fails or a chapter is not found, the LLM receives no direction on what to try next.