vision-mcp

by konan-1947

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a specific image understanding task (charts, general, UI, errors, OCR, diagrams) with no overlap, ensuring agents can easily select the right one.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with clear, descriptive names like analyze_data_visualization and extract_text_from_screenshot, making the set predictable.
Tool Count5/5
With 6 tools, the server is well-scoped for a general vision assistant, covering common image analysis needs without being bloated or sparse.
Completeness4/5
The tool set covers major vision tasks (description, OCR, diagrams, errors, UI, data viz), though advanced manipulation or more niche domains are absent. Minor gap.
Average 3.6/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 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?
No annotations provided, so description must bear full burden. It states 'Read' implying non-destructive operation but does not disclose limitations (e.g., supported formats, accuracy, or whether it returns raw data vs. summary). Insufficient for safe invocation.
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?
Single, front-loaded sentence with no fluff. Every word earns its place: verb, target, output.
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?
Purpose is clear but lacks behavioral details (e.g., return format, constraints) and no output schema. With no annotations, more context about what agents can expect would be beneficial.
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% with descriptions for both parameters (image_path and question). The tool description adds no additional meaning beyond the schema, so baseline score of 3 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?
The description clearly states the verb 'Read' and specific resources 'chart, graph, or dashboard screenshot', and defines the output as 'extract key values, trends, and insights'. This distinguishes it from sibling tools like analyze_image (generic) or diagnose_error_screenshot (errors).
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?
No explicit guidance on when to use this tool versus siblings such as 'analyze_image' or 'understand_technical_diagram'. The description implies use for data visualizations but does not provide when-not or alternatives.
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 provided, the description carries full burden. It discloses that the tool performs OCR and returns text verbatim, implying it is read-only and non-destructive. However, it does not detail behavior on invalid input, failure modes, or any side effects, leaving gaps in 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 a single, well-structured sentence containing the core action, resource, and examples. It is concise with no unnecessary words, making it easy to parse.
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 the simplicity of the tool (2 parameters, no output schema, no annotations), the description covers the core purpose adequately but lacks details on return format or error handling. It is minimally complete but could be enhanced.
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%, as both parameters have descriptions in the input schema. The description adds no additional meaning beyond what the schema already provides, so the baseline of 3 is appropriate.
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 action (OCR) and the resource (screenshot), with examples of suitable content (code, terminal output, document). It effectively conveys the tool's primary function, though it does not explicitly differentiate from siblings like 'analyze_image' or 'describe_ui'.
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 explicit guidance on when to use this tool vs. alternatives (e.g., 'analyze_image', 'describe_ui'). It implies usage from the examples but does not state when not to use it or offer clear context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It only states the tool 'reads' and 'explains', but does not mention any constraints (e.g., image format, clarity requirements), side effects, or return format. This is minimal beyond purpose.
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 a single 14-word sentence, front-loaded with the verb 'Read'. Every word is meaningful, and there is no redundancy or unnecessary detail.
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?
Given no output schema and no annotations, the description should explain what the tool returns (e.g., a textual explanation) and mention any limitations. It only says 'explain its components and relationships', which is vague. The tool is simple (2 params) but still incomplete for an agent to fully understand the behavior.
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 100%; both parameters have descriptions in the schema. The tool description adds no additional meaning or context for the parameters. Baseline 3 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?
Description clearly states the verb 'Read' and 'explain', the resource 'technical diagrams' (architecture, flowchart, UML, ER diagram), and the outcome 'components and relationships'. It distinguishes from siblings like analyze_data_visualization (data charts) and describe_ui (UI elements) by specifying diagram types.
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?
Description implies use for technical diagrams by listing types, but provides no explicit when-to-use or when-not-to-use guidance nor comparison to sibling tools. The agent must infer usage from the listed types.
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 partially carries the burden. It reveals the tool's output (description plus optional code) but does not confirm it is read-only, describe any side effects, or mention rate limits. The behavioral traits are implied but not fully transparent.
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 with no fluff. The main action is front-loaded, and the optional behavior is added concisely. Every sentence earns its place.
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 no output schema, the description should specify output format. It mentions 'description' and 'sketch' but does not clarify if the output is plain text or structured. Also, it could provide more context on image path requirements (e.g., supported formats). Adequate but with gaps.
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?
Schema coverage is 100%, so baseline is 3. The description adds value beyond the schema by explaining how to use the 'question' parameter to request code generation ('Ask for code/markup to also get a JSX/HTML+CSS sketch'). This aids correct usage.
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 'describe' and specific resource 'UI screenshot', covering layout, components, and style. It also distinguishes from sibling tools like 'analyze_data_visualization' by focusing on UI, making it easy to select.
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 lacks explicit when-to-use or when-not-to-use guidance. It does not compare with siblings or mention alternatives. The only hint is the phrase 'UI screenshot', which implicitly limits usage but is not sufficient for clear decision-making.
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 the full burden. It states the tool analyzes and suggests, but does not disclose limitations, output format, or processing characteristics. Adequate but not detailed.
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?
A single sentence efficiently conveys the purpose and scope. No unnecessary words; all information serves the function. Excellent conciseness.
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?
No output schema exists, so the description should clarify what the tool returns. It mentions 'suggest the cause and a fix' but not whether output is plain text, structured JSON, or something else. Slightly incomplete.
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 covers 100% of parameters with descriptions. The tool description adds no significant meaning beyond what the schema already provides. Baseline 3 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?
The description clearly states the verb 'analyze', the resource 'screenshot of an error message, stack trace, or crash dialog', and the outcome 'suggest the cause and a fix'. It effectively distinguishes from sibling tools like 'analyze_image' or 'extract_text_from_screenshot'.
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 for error screenshots but provides no explicit when-to-use or when-not-to-use guidance. Alternatives among siblings are not mentioned, so the agent must infer context.
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 provided, the description carries the full burden of behavioral disclosure. It accurately states the tool describes or answers questions about images, but does not mention any limitations (e.g., file size, format support, accuracy, or response format). While not misleading, it lacks detail beyond the core function.
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, using only two short sentences with no redundant information. Every word adds value, and the key information (purpose and usage context) is front-loaded.
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 (2 parameters, no output schema, no nested objects), the description is adequate. It covers the general use case and provides usage context. However, it could mention potential failure modes (e.g., unclear images) or the fact that it handles both description and question answering.
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 100%, so the schema already documents both parameters. The description adds minimal extra meaning: it clarifies that 'question' is optional and that the tool provides a general description if omitted. This is helpful but not substantial beyond the schema.
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 ('Describe an image or answer a specific question about it') and explicitly distinguishes it from specialized siblings by labeling it as a 'General-purpose vision tool for when no more specific tool applies.' This provides specific verb+resource and differentiation.
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 says 'for when no more specific tool applies,' which gives clear guidance on when to use this tool versus the specialized alternatives listed in sibling tools. However, it does not explicitly state when not to use it or list conditions that would make a more specific tool preferable.
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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