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xiayangqun

read-image-mcp

by xiayangqun
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Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusing it with others. The tool's purpose is clearly defined, so an agent can unambiguously select it when needing to read an image.

    Naming Consistency5/5

    The single tool name 'read_image' follows a clear verb_noun pattern, which is consistent. There are no other tools to create inconsistencies.

    Tool Count3/5

    One tool is on the lower end of typical tool counts. While the server's purpose is narrow, a single tool feels thin; however, it is appropriate for the specific functionality provided.

    Completeness5/5

    The tool covers all common input methods for images (path, URL, base64, data URL), so there are no obvious gaps in functionality for its stated purpose.

  • Average 2.8/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • 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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  • 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

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description is the sole source of behavioral information. It discloses the ability to handle different source types, but nothing about permissions, side effects, rate limits, or return format. This minimal disclosure is inadequate for a tool with no other behavioral annotations.

    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 exceptionally concise, using two short sentences to convey the core function and supported sources. Every word adds value, making it a model of efficiency.

    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?

    The tool has 5 parameters and nested objects, yet the description only addresses the source type. It omits explanations of mode options, the question field, raw_response, and the schema parameter. This incompleteness for a moderately complex tool is a significant gap.

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

    Parameters2/5

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

    The schema has 5 parameters, and the description only hints at source type possibilities, not the mode, question, schema, or raw_response fields. Given the 0% schema description coverage, the description fails to compensate for most parameters.

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

    Purpose4/5

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

    The description clearly states the tool's function: reading an image through an OpenAI-compatible vision model. It also specifies supported source types (path, url, base64, data_url), which adds precision. Without sibling tools, it doesn't need to differentiate.

    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 no guidance on when to choose this tool over alternatives, nor any context for its appropriate use cases. It doesn't mention exclusions or prerequisites, leaving the agent without decision-making information.

    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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Glama performs regular codebase and documentation scans to:

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

Our badge communicates server capabilities, safety, and installation instructions.

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