Loading ToolBench
A Python MCP server that dynamically loads tools from a Python module and exposes them via FastMCP HTTP transport with OpenAI client integration
The single tool 'add' has a clear, action-oriented name and a concise description. Input schema is present with typed parameters and descriptions. However, the tool is extremely simple (basic arithmetic), the description is brief (27 chars, below the ideal 50 - 200 char range for LLM optimization), and there is no documented output schema. The tool composition is sound for its narrow scope, but lacks the richness expected of production-grade agent tools. Error handling and edge cases (e.g., integer overflow) are not discussed.
Return the sum of two integers.
Tool description is too brief (27 chars). LLM-optimized descriptions should be 50 - 200 chars and clarify WHAT the tool does, WHEN to use it, and WHAT it returns. Current description only states the action, not context or return format.
Output schema is not documented. The tool returns an integer sum, but the LLM has no schema to verify the return type. This forces the agent to infer the structure.
No error handling guidance. The description does not mention edge cases, constraints, or what happens if inputs overflow or are invalid. Error responses should guide recovery.
No parameter constraints documented (e.g., integer range limits). LLMs cannot infer whether huge integers are accepted or what happens on overflow.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 58 | 2025-06-18+ | v2 |
| 2026-03-09 | F | 0 | - | v1 |