An enterprise-ready Model Context Protocol Gateway that integrates multiple MCP servers and provides a unified interface for AI agents
Critical failures across all three tools. Tool definitions appear to be inferred from a Makefile rather than explicitly registered in source code. No actual tool implementation visible, only names, descriptions, and schemas listed in metadata. Input schemas are present but lack proper JSON Schema structure (missing 'type' declarations on parent objects). Descriptions are generic and under 100 chars. No evidence of parameter validation, error handling, or output schema documentation. This server cannot be evaluated as production-ready.
Perform basic mathematical operations on operands
Evaluate mathematical expressions with variables
Perform statistical operations on datasets
Tool definitions inferred from Makefile metadata, not explicitly registered in source code. No actual tool implementation visible. Per HARD SCORING RULE: cap inferred tools at 50 overall.
Input schemas lack proper JSON Schema structure. Parent objects missing 'type' field (should be 'object'). Parameters present but schema is malformed. Per HARD SCORING RULE: parameters without type definitions cap schema score at 30.
Descriptions are generic and under 100 characters. 'Perform basic mathematical operations' lacks WHEN to use, WHAT it returns, or prerequisites. Per HARD SCORING RULE: descriptions under 20 chars score 0-20; these are borderline.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 44 | 2026-07-28+ | v2 |
No output schema documented. LLMs cannot plan downstream calls or extract results. basic_math returns what? A number? An object with result and metadata? Unknown.
No error handling guidance. If expression_eval receives invalid syntax, what error is returned? How should the LLM recover? No recovery guide present.