Refine Prompt
Server Quality Checklist
Latest release: v1.2.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined and distinct by default.
Naming Consistency5/5The single tool name 'refine_prompt' follows a consistent verb_noun pattern, and there are no other tools to create inconsistency.
Tool Count2/5A single tool feels thin for a server named 'Refine Prompt', as it suggests a narrow scope with no additional functionality like versioning, history, or batch processing. This is borderline too few for a dedicated server.
Completeness3/5The tool covers the core action of refining prompts, but there are notable gaps such as no ability to list, retrieve, or manage previous refinements, which limits workflow completeness for a prompt refinement domain.
Average 4/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool's function and transformation process but lacks details on behavioral traits such as rate limits, error handling, or output format. The description doesn't contradict annotations (none exist), but it provides only basic operational context without deeper behavioral insights.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the core purpose stated in the first sentence. Each sentence adds value: the first defines the tool's function, the second explains the transformation, and the third provides usage triggers. There's minimal redundancy, though it could be slightly more concise by combining some phrases without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description covers purpose and usage well but lacks details on behavioral aspects and output. It doesn't explain what the refined prompt looks like or any constraints, which would be helpful since no output schema exists. This makes it adequate but with gaps in completeness for an agent's full understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents both parameters thoroughly. The description doesn't add any additional meaning or context beyond what the schema provides (e.g., no examples, edge cases, or usage tips for parameters). This meets the baseline score of 3, as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('refine, rewrite, improve, enhance, or optimize a prompt') and identifies the resource ('prompt'). It explicitly distinguishes what the tool does ('transforms raw prompts into more effective versions') and how it achieves this ('clearer, more detailed, and better structured to improve results from LLMs'). No siblings exist, but the description is comprehensive and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidelines with clear triggers ('whenever a user asks to refine, rewrite, improve, enhance, or optimize a prompt') and specific phrases to watch for ('refine prompt' or similar phrases). It directly states 'MUST be used' for these cases, offering definitive when-to-use instructions. Since no sibling tools exist, no alternative guidance is needed, making this complete for the context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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