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llt22

image-recognition-mcp

by llt22
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TypeScript
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.2.0

  • Disambiguation5/5

    With only one tool, there is no ambiguity; the agent will always select the correct tool.

    Naming Consistency5/5

    A single tool name is trivially consistent with itself, as there is no other tool to conflict.

    Tool Count3/5

    One tool for image recognition is thin but acceptable if the tool is versatile. The tool handles many input formats, but more specialized tools might be expected.

    Completeness4/5

    The tool covers image recognition well with multiple input methods, but lacks additional operations like model selection or detailed output control.

  • Average 3.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 22 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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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, the description must disclose all behavioral traits. It mentions reading clipboard if no image is provided and supported input types, but does not describe potential side effects, read-only status, model limitations, or cost implications. The description is insufficient for full behavioral transparency.

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

    Conciseness5/5

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

    The description is extremely concise with two sentences. The first sentence front-loads the core purpose, and the second adds essential context on input sources. Every sentence contributes value with no wasted words.

    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?

    Despite having no output schema, the description fails to explain what the tool returns after analysis (e.g., text description). It also does not cover potential limitations, authentication, or rate limits. The description is incomplete for a tool with 4 parameters and no output schema.

    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?

    Schema coverage is 100%, so the baseline is 3. The description reinforces the default clipboard behavior and supported formats already covered in the schema, but adds no new semantic meaning beyond what the schema provides. Thus, it meets the baseline without exceeding it.

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

    Purpose5/5

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

    The description clearly states the tool's purpose: recognizing and analyzing images using a vision model, with specific details about input sources. It is a specific verb+resource combination that leaves no ambiguity about 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 Guidelines3/5

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

    The description implies usage by explaining default clipboard behavior and supported formats, but lacks explicit guidance on when to use this tool versus alternatives (none provided) or when not to use it. It provides basic usage context but no exclusions.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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