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zgz518

vision-bridge-mcp

by zgz518
OverviewSchemaRelated ServersScoreDiscussions
Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation4/5

    The two tools have clearly distinct primary purposes: look_at_image for general image understanding and extract_text_from_image for exact OCR transcription. There is a minor overlap when an image contains text, but the explicit OCR tool removes most ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with lowercase snake_case (look_at_image, extract_text_from_image). The naming is predictable and clearly reflects each tool's function.

    Tool Count3/5

    With only two tools, the server feels minimally scoped. While the narrow focus on vision tasks is reasonable, the count is at the lower boundary and leaves the set feeling thin rather than comprehensive.

    Completeness4/5

    The server covers the two most essential vision bridge capabilities—image understanding and text extraction. However, other potentially useful operations like object detection or image comparison are absent, leaving minor gaps in the surface.

  • Average 4.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 6 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

  • Behavior3/5

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

    The description discloses the core behavior (OCR, verbatim transcription) and the parameter format, which gives agents some sense of what to expect. However, with no annotations provided, it does not disclose potential limitations (e.g., image format support, accuracy issues) or side effects, leaving a gap in 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.

    Conciseness4/5

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

    The description is short and front-loaded with the tool's purpose and a mandatory-call directive. The Args section is clear and direct. Minor redundancy exists with the '必须调用' phrase appearing twice, but it does not significantly detract from the structure.

    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 (single parameter), the description covers purpose, usage, and parameter semantics well. An output schema exists, so return details are not required. It lacks discussion of edge cases like unsupported image types or error handling, but for a straightforward OCR tool, the description is reasonably complete.

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

    Parameters5/5

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

    The input schema provides no description for the 'image' parameter (0% coverage). The description fully compensates by stating the parameter accepts a local absolute file path or an http(s) URL, explicitly defining the valid formats. This is essential for correct invocation.

    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 explicitly labels the tool as an OCR tool and states it transcribes all text in an image verbatim, with suitable use cases listed (screenshots, documents, code, receipts). The directive '必须调用' (must call) reinforces its specific role, clearly distinguishing it from the sibling tool 'look_at_image', which handles visual inspection rather than text extraction.

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

    Usage Guidelines4/5

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

    The description provides an explicit when-to-use rule: it must be called whenever a user uploads an image requiring text extraction. However, it does not mention when not to use the tool or explicitly compare it to 'look_at_image', so it stops short of full alternative guidance.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

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

    With no annotations provided, the description carries the behavioral burden. It explains that the tool accepts local paths or URLs, returns a text description, and that the prompt parameter focuses attention. It does not cover error cases or edge limitations, but provides solid transparency for a vision interpretation tool.

    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 front-loaded with a mandatory-call marker and purpose, then dives into clear parameter explanations. Every sentence adds value, and the structure is easy to scan.

    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?

    The description covers the core use cases, parameter semantics, and behavioral expectations. It could be more complete by explicitly addressing sibling tool differentiation and error/limitation scenarios, but it is strong for a moderately simple tool with an output schema.

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

    Parameters5/5

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

    Schema coverage is 0%, and the description fully compensates. It explains the image parameter with absolute path examples and URL support, and describes the prompt parameter's default behavior. This goes well beyond the bare schema definition.

    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 purpose: understand an image and return a text description. It distinguishes itself from the sibling extract_text_from_image by focusing on content understanding rather than text extraction, though it does not explicitly name the sibling.

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

    Usage Guidelines4/5

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

    The description gives explicit when-to-use guidance: when the user uploads/pastes an image or mentions a file path, and even instructs the agent not to refuse. However, it lacks when-not-to-use guidance or explicit mention of the alternative extract_text_from_image.

    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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