vision-mcp

Server Quality Checklist

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Latest release: v0.0.3
Disambiguation5/5
Each tool has a clearly distinct purpose: general analysis, comparison, region analysis, OCR, and metadata inspection. There is no overlap in functionality.
Naming Consistency5/5
All tool names follow the consistent pattern 'vision_<verb>' or 'vision_<verb>_<noun>', using lowercase and underscores. The naming is predictable and readable.
Tool Count5/5
With 5 tools, the server is well-scoped for image analysis. Each tool addresses a core need without unnecessary clutter.
Completeness5/5
The tool set covers all essential image analysis operations: general analysis, comparison, region analysis, OCR, and metadata inspection. The vision_capabilities task type also provides self-description.
Average 4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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?
No annotations provided, so description carries full burden. Discloses use of dedicated OCR model with automatic fallback to VLM, and mentions output is structured by reading order. Lacks details on authentication, rate limits, or behavior with empty images.
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?
Three sentences, each with a distinct purpose: function definition, use cases, model behavior. No filler, front-loaded with the core action. Conciseness earned.
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 5 parameters and 0% schema coverage, description omits parameter details and output specifics beyond 'structured text'. Output schema exists but not leveraged. Tool complexity warrants more explanation for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and description does not explain any of the 5 parameters (language, include_raw, image_source, etc.). Adds no meaning beyond the schema itself, which also lacks descriptions. Baseline expectation: description should compensate.
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?
Clearly states it extracts text from images via OCR and returns structured text by reading order. Lists specific use cases (screenshots, documents, receipts, etc.) and distinguishes from sibling tools like vision_analyze which analyze images rather than extract text.
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?
Explicitly recommends using for text-heavy images with examples. Does not specify when not to use or provide alternative tools, but the context and sibling list imply guidance. Lacks exclusion criteria like 'if no text present'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It mentions return format (JSON envelope with summary, observations, etc.) and input support, but does not explicitly state read-only nature, rate limits, or side effects. The reference to README helps, but behavioral transparency is adequate but not thorough.
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 concise and well-structured: a clear first sentence for purpose, followed by supported inputs and task types in a bullet-like list. No extraneous information, and all sentences add value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 6 parameters and an output schema. The description covers purpose, inputs, and task types, and references the README for output schema. However, unexplained parameters (detail, prompt, include_raw, include_source_ref) and the absence of behavioral constraints make it somewhat incomplete for a complex tool.
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?
Schema description coverage is 0%, so the description should compensate. It explains only 'image_source' and 'task' (task types), leaving 'detail', 'prompt', 'include_raw', and 'include_source_ref' unexplained. The default prompt in Chinese may confuse. Given the low coverage, the description adds insufficient parameter meaning.
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 analyzes images using a vision-language model, enumerates supported input types, and lists specific task types (general, qa, ui, chart, document, object, screenshot, code_screenshot). This distinguishes it from sibling tools like vision_extract_text (OCR) and vision_compare, making the purpose specific and well-defined.
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 specifies task types that guide model behavior, implicitly advising when to use this tool (e.g., for general analysis, UI analysis, chart analysis). However, it does not explicitly state when not to use it or directly compare with alternatives, so guidance is present but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions returning structured differences with confidence levels, which adds transparency, but lacks details on side effects, prerequisites, or limitations (e.g., supported formats, performance).
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?
Description is concise with bullet points for use cases and ends with a clear statement about return values. Every sentence adds value, and the structure is front-loaded with the core purpose.
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 complexity (5 params, output schema exists), the description covers the main purpose and return format, but parameter guidance is lacking. Overall adequate for a comparison tool, though more detailed parameter descriptions would improve completeness.
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?
Schema coverage is 0% (no parameter descriptions). The description does not explain parameters beyond the schema (names, types, defaults). For a tool with 5 parameters, this is insufficient; the description should at least clarify 'focus', 'prompt', 'include_raw', and 'include_source_ref'.
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?
Description clearly states the tool compares two or more images and identifies differences, listing specific use cases like UI regression testing and design comparison, which distinguishes it from sibling tools like vision_analyze or vision_extract_text.
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?
Description provides explicit use cases (UI regression, design vs implementation, bug screenshot, version diff) but lacks guidance on when not to use or alternatives; context from sibling tools implies other tools for analysis, but no exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It explains normalized coordinates and the VLM analysis but does not state that the operation is non-destructive, mention authentication or rate limits, or describe potential latency. The ambiguity around 'crop' (virtual vs. actual modification) is not resolved.
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 composed of three short paragraphs, each with a clear focus: purpose, coordinate details, and workflow. It is concise without wasted words, though it could be slightly more structured with bullet points or sections.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 parameters and an output schema, the description covers the main behavior and coordinate system but omits explanation of several optional parameters and does not describe the return format. The workflow guidance adds context, but the lack of full parameter coverage affects completeness.
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 description coverage is 0%, so the description must compensate. It explains the critical coordinate parameters (x, y, width, height) are normalized 0-1, which is helpful. However, it does not explain other parameters like task, prompt, include_raw, or include_source_ref, leaving gaps for the agent.
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 action: 'Crop a region of an image and analyze it with VLM.' It also specifies use cases like inspecting small text, UI elements, and chart data, and distinguishes from sibling tools by positioning it as a zoom-in tool after broader analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit workflow guidance is provided: 'Use vision_inspect first to get dimensions, then vision_analyze for overview, then vision_crop_analyze to zoom into specific regions.' This tells the agent exactly when and in what order to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully bears the burden. It explicitly states that the tool does NOT call a VLM, implying it is lightweight and non-destructive. This is critical behavioral context.
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?
Two sentences, no wasted words. The key information is front-loaded: purpose first, then usage guidance. Every sentence adds value.
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?
For a simple metadata inspection tool with an output schema, the description covers purpose and usage well. However, the lack of parameter documentation is a minor gap. Overall, it is complete enough for the tool's simplicity.
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?
Schema coverage is 0% (no descriptions in input schema), so description must compensate. However, the description does not explain what 'image_source' expects (e.g., file path, URL) or the effect of 'include_source_ref'. The agent receives no added meaning beyond parameter names.
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 it inspects image metadata (dimensions, format, size, mode) and distinguishes itself from detailed analysis tools like vision_analyze. The verb 'inspect' plus specific metadata fields make the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises using this before detailed analysis to understand dimensions and plan crop coordinates. This provides clear context for when to use this tool versus siblings like vision_analyze or vision_crop_analyze.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided. The description suggests a read-only operation by using 'Return', but does not explicitly state it has no side effects or require authentication. More detail would help.
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?
Two concise sentences that front-load the purpose and usage. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no parameters and an output schema defined, the description sufficiently covers what the tool returns and when to use it. Complete for this simple discovery tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist; schema coverage is 100%. The description adds no parameter info because none are needed. Baseline score of 4 is appropriate.
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?
Clearly states it returns server capabilities, supported models, and limits. Distinguishes from sibling tools which are analysis tools, making this a discovery tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises to call this before using other tools, providing direct usage context.
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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