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masachika-kamada

mcp-local-image-reader

by masachika-kamada
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

67%
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  • Latest release: v0.1.1

  • Disambiguation5/5

    With only one tool, there is no risk of ambiguity or confusion for an agent.

    Naming Consistency5/5

    The single tool name 'read_image' follows a clear verb_noun pattern and is consistent within itself.

    Tool Count4/5

    A single tool is slightly thin, but for a specialized image reading server, it is well-scoped and appropriate.

    Completeness3/5

    The tool covers the core functionality of reading images, but lacks auxiliary capabilities like listing available images or checking file existence.

  • Average 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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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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, the description carries full burden. It discloses that the tool reads from the filesystem, returns base64 ImageContent, and supports specific formats. It does not mention error handling (e.g., file not found, unsupported format) or permissions, but for a simple read operation, the disclosure is fairly comprehensive.

    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 two sentences, front-loaded with the core action. Every sentence adds value: the first explains what it does and output format, the second lists supported formats. No redundant or verbose language.

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

    Completeness4/5

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

    Given the tool's simplicity (one parameter, no output schema, no annotations, no siblings), the description is adequate. It covers purpose, input, output format, and supported types. It could mention that the operation is read-only and safe, but overall it is complete enough for an agent to use correctly.

    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 input schema already describes the 'file_path' parameter as 'Absolute path to the image file'. The description does not add new meaning beyond this, so it meets the baseline of 3 for 100% schema coverage.

    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 verb 'Read', the resource 'image from the filesystem', and the output 'base64-encoded ImageContent'. It lists supported formats, making the purpose unambiguous. No sibling tools exist, so differentiation is not needed.

    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 when needing to read an image as base64, but it does not provide explicit guidance on when to use this tool versus alternatives, or any prerequisites or exclusions. No siblings exist, so the lack of alternatives is not a penalty, but the description could still benefit from stating typical use cases.

    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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