MCP server for promptune — exposes enhance and score tools for AI-powered prompt enhancement
Two tools with basic schemas and descriptions, but significant gaps in parameter documentation and output schema clarity. enhance_prompt has a reasonable description (89 chars) and three parameters with types and defaults, but lacks output schema documentation. score_prompt_quality has a strong description (156 chars) but minimal parameter detail. Neither tool documents return structure, pagination, or error recovery paths. Parameter descriptions are present but generic. No tool annotations (readOnlyHint/destructiveHint) despite both being READ_ONLY. Missing guidance on when to use each tool vs. alternatives.
Enhance a prompt using AI (3-tier: rules, local LLM, cloud).
Score a prompt across 7 quality dimensions (0-100). Returns total PQS score, detected intent, and per-dimension breakdown with actionable suggestions.
Output schemas not documented. Neither tool specifies what fields are returned, their types, or structure. LLMs cannot plan downstream operations or extract required data.
score_prompt_quality parameter 'prompt' lacks description. Parameter descriptions are mandatory for LLM understanding; a bare parameter name is insufficient.
No error handling guidance. Tools do not document failure modes, recovery steps, or what the LLM should do if enhancement fails or scoring is unavailable.
Tool annotations missing. Both tools are READ_ONLY but lack readOnlyHint annotation. This prevents clients from optimizing caching and retry logic.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 53 | 2026-07-28+ | v2 |
enhance_prompt 'tier' parameter description is technical and lacks guidance on when to use auto vs. forced tiers. LLMs need context on cost/latency tradeoffs.