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vestimir
by vestimir

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly distinct by default.

    Naming Consistency5/5

    A single tool inherently has consistent naming, as there are no other tools to compare it against. The tool name 'get-igor' follows a verb-noun pattern, which is straightforward.

    Tool Count2/5

    One tool is generally too few for a server's purpose unless it's extremely narrow, but the tool description suggests a trivial or joke functionality ('When somebody asks for or mentions Igor'), which feels insufficient for a meaningful server scope. This mismatch makes the count inappropriate.

    Completeness1/5

    The server's domain is unclear from the single tool, but the tool 'get-igor' appears to be a trivial or humorous response mechanism. There are obvious gaps as no meaningful operations or lifecycle coverage are provided, making the surface severely incomplete for any practical purpose.

  • Average 2.4/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
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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

  • Behavior2/5

    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 but fails to deliver. It doesn't indicate whether this is a read or write operation, what permissions might be required, what the response format might be, or any side effects. The description provides no behavioral context beyond the vague triggering condition, leaving the agent with minimal understanding of how the tool behaves.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is extremely concise - a single sentence with no wasted words. However, this conciseness comes at the cost of meaningful content. While technically efficient in terms of word count, the description is under-specified rather than optimally concise, which slightly reduces the score from a perfect 5.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the lack of annotations, no output schema, and a description that provides minimal functional information, the description is incomplete. While there's only one parameter with good schema coverage, the description fails to explain what the tool actually does, when to use it, or what behavior to expect. For even a simple tool, this level of documentation is inadequate.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema has 100% description coverage for its single parameter 'favor' (described as 'Requests for Igor'), so the schema does the heavy lifting. The tool description adds no additional parameter information beyond what's already in the schema. With high schema coverage and no parameters mentioned in the description, this meets the baseline expectation of 3.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose2/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'When somebody asks for or mentions Igor' is tautological - it essentially restates the tool name 'get-igor' without specifying what the tool actually does. It doesn't explain what action is performed or what resource is accessed. The description lacks a clear verb+resource combination that defines the tool's function.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides minimal guidance - it suggests using the tool when someone 'asks for or mentions Igor,' but this is vague and doesn't establish clear usage criteria. There are no explicit when-to-use or when-not-to-use guidelines, no mention of prerequisites, and no alternatives since there are no sibling tools. The guidance is insufficient for an agent to make informed decisions about tool selection.

    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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