Copilot Leecher
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'request_review' has a clear, distinct purpose that cannot be mistaken for any other functionality.
Naming Consistency5/5A single tool inherently exhibits perfect naming consistency as there are no other tools to compare against. The name 'request_review' follows a clear verb_noun pattern that would be consistent if more tools existed.
Tool Count2/5One tool is generally too few for most server purposes, as it severely limits functionality and scope. While the tool's purpose is clear, a single tool feels thin and incomplete for handling complex workflows, suggesting an extreme mismatch with typical server design.
Completeness1/5The server appears to focus on review workflows, but with only one tool for requesting reviews, there are severe gaps. Missing are tools for submitting work, checking review status, handling feedback, or managing the review process, making the surface incomplete and likely to cause agent failures.
Average 3.6/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the tool sends the session to a review server, requires waiting for expert feedback via a Web interface, and outlines possible feedback outcomes ('ok' or 'approved' for pass, otherwise need for improvement). It also implies an asynchronous process with human intervention. However, it lacks details on error handling, timeouts, or authentication needs.
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, starting with the core purpose. It uses two sentences to explain the process and outcomes efficiently, with no redundant information. However, it could be slightly more structured by separating the workflow steps more clearly, which prevents a perfect score.
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 complexity (involving human review and asynchronous feedback) and the absence of annotations and output schema, the description provides a basic overview of the process and outcomes. However, it lacks details on error scenarios, response formats beyond 'ok' or 'approved', or how to handle multiple review cycles, leaving gaps in completeness for effective agent use.
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?
The input schema has 100% description coverage, providing clear documentation for both parameters (taskId and summary). The description does not add any additional meaning or context beyond what the schema already specifies, such as explaining how these parameters are used in the review process. Thus, it meets the baseline score of 3 for high schema coverage without compensating with extra insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '请求专家审查你完成的工作' (Request expert review of your completed work). It specifies the verb ('请求审查' - request review) and resource ('你完成的工作' - your completed work). However, since there are no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied usage guidelines by explaining the workflow: call the tool, wait for expert feedback, and if feedback is not 'ok' or 'approved', improve work and request review again. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., other review methods) or prerequisites, as there are no sibling tools to compare against.
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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- Evaluate tool definition quality.
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