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An MCP server that manages async LLM connection pooling and rate limiting for multiple LLM providers
This server has critical definition failures. The single tool 'execute_skill_action' has no visible input schema, no parameter descriptions, and a generic 1-line description (18 chars, below the 20-char minimum). The mcp_server.py file contains only a JSON print statement with no actual MCP protocol implementation, no tool registration, no request handling, no schema definitions. The tool definition is inferred from example_usage.py, not explicitly registered. This is a stub implementation, not a functional MCP server.
Execute a skill action for LLM connection pooling and rate limiting
No input schema visible for execute_skill_action. Tool accepts parameters (provider, model, requested_tokens) but no JSON Schema definition exists in the code.
Tool description is 18 characters ('Execute a skill action for LLM connection pooling and rate limiting'), below 20-char minimum and lacks actionable context for LLM selection.
No parameter descriptions. The tool accepts provider, model, and requested_tokens but provides no guidance on valid values, formats, or constraints.
Tool definition is inferred from example_usage.py, not explicitly registered in mcp_server.py. Per hard scoring rules, inferred tools cap at 50 overall.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 17 | 2026-07-28+ | v2 |
No output schema documented. The response structure (lease_id, granted, queue_wait_ms, etc.) is not formally defined, forcing LLMs to infer field types and availability.