Loading ToolBench
A Spring Boot-based MCP server that generates data based on prompts and provides user data generation capabilities
Critical gaps across all dimensions. Tool descriptions are minimal (10-20 chars, well below 50-200 char baseline for LLM optimization). Input schemas present but parameters lack descriptions entirely, violates core pattern:tool-description requirement. No output schemas documented. No error handling guidance. Tool names lack clarity on what data is generated or returned. This server would require substantial rework before production use.
Generates data based on the provided prompt.
Generate User data based on the provided id and age.
All parameter descriptions missing. 'prompt' and 'id'/'age' parameters have no descriptions explaining what values are expected, valid ranges, or formats. LLMs cannot infer parameter semantics from names alone.
Tool descriptions are too brief (10-20 chars). 'Generates data based on the provided prompt' and 'Generate User data based on the provided id and age' lack context on WHEN to use each tool, what distinguishes them, and what output structure to expect. Baseline for LLM-optimized descriptions is 50-200 chars.
No output schemas documented. LLMs cannot plan downstream tool calls or extract structured data without knowing what fields are returned. Both tools lack return type documentation.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 27 | 2026-07-28+ | v2 |
Tool naming lacks clarity. 'generateData' is generic, does it generate synthetic test data, mock user profiles, or random strings? 'getUserData' is ambiguous, does it fetch existing user data or generate new user records? Names should start with action verbs and clearly indicate the operation.
No error handling guidance. No documentation of what errors can occur, whether they are retryable, or what the LLM should do next. Agents have no recovery path on failure.