High-performance MCP (Model Context Protocol) server built with Fastify, TypeScript, and functional programming. Production-ready with authentication, metrics, and auto-discovery capabilities for AI agents and LLM applications.
This server has only 2 tools (echo, health), both READ_ONLY, with minimal schema documentation and generic descriptions. Tool names lack action verbs and fail to communicate intent clearly. No visible parameter descriptions, no structured output schemas, and no error handling guidance. The server emphasizes infrastructure (Fastify, auth, metrics) over tool quality. Based on conservative rubric calibration, a 38 reflects major definition gaps across both tools.
Tool names do not start with action verbs. 'echo' and 'health' are nouns, not verb_noun patterns like 'get_health' or 'send_echo'. LLMs cannot infer intent from these names alone.
No input schemas visible in source code. The files src/tools/echo.ts and src/tools/health.ts are not provided, so parameter types, constraints, and descriptions cannot be verified.
Tool descriptions are generic or missing. Without seeing the actual tool registration in echo.ts and health.ts, descriptions cannot be assessed, but based on naming alone (bare 'echo', 'health'), they are likely too generic or absent to guide LLM selection.
No output schema documentation. Without seeing the tool implementations, return types and field structures are unknown. LLMs cannot plan downstream calls or extract needed data.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-21 | F | 29 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 28 | - | v1 |
No error handling guidance visible. No evidence of recovery suggestions, error classification, or actionable error messages in either tool.
Tool implementation files (src/tools/echo.ts, src/tools/health.ts) are not provided in the source code sample. Cannot verify actual schema registration, parameter validation, or output structure.