z_ai_vision_mcp_server_clone

Server Quality Checklist

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Latest release: v0.1.1
Disambiguation4/5
Each tool has a distinct purpose (e.g., data visualization vs. error screenshot vs. text extraction), but 'analyze_image' as a catch-all creates slight ambiguity since it overlaps with the specialized tools.
Naming Consistency4/5
Most names follow a verb_noun pattern (analyze_*, extract_text_*, understand_*), but 'ui_diff_check' and 'ui_to_artifact' deviate slightly from the typical structure.
Tool Count5/5
With 7 tools focused on image and UI analysis, the count is well-scoped—no unnecessary duplication and full coverage of the domain.
Completeness5/5
The tool set covers major visual analysis tasks (charts, screenshots, text extraction, UI comparison, diagram understanding) with no obvious gaps for the intended use case.
Average 2.6/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 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
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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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits such as side effects, authentication needs, or output format. It fails to mention any, leaving the agent uninformed about what happens when the tool is invoked.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short (12 words), but it sacrifices informativeness for brevity. It does not earn its place as it fails to provide necessary context for usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and two undocumented parameters, the description is severely incomplete. An agent cannot safely or effectively use this tool based on the provided information.
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?
The input schema has 0% description coverage, and the tool description does not explain the role of 'image_source' or 'prompt'. This leaves the agent without understanding how to construct a valid request.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description indicates it is a general-purpose image analysis tool, contrasting with specialized siblings. However, it lacks specificity about what analysis entails (e.g., object detection, color analysis), making the purpose vague.
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 explicitly states to use this tool 'for cases not covered by the specialized image tools,' providing clear context for when it is appropriate. It does not elaborate on specific exclusion criteria or name alternatives, but the guidance is direct.
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?
Description omits behavioral details such as output format, required input quality, or side effects. Since no annotations exist, this gap is significant.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence is concise but overly terse, sacrificing necessary detail. It is front-loaded but incomplete.
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 the absence of annotations, output schema, and parameter descriptions, this description is insufficient for an agent to correctly invoke the tool. It lacks details on inputs and outputs.
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?
With schema description coverage at 0%, the description adds no meaning to the three parameters. It does not explain what 'image_source', 'prompt', or 'analysis_focus' mean or their expected values.
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?
Description clearly specifies the resource ('charts, graphs, and dashboards') and action ('analyze for metrics, trends, anomalies, and implications'). It differentiates from sibling tools like 'analyze_image' by focusing on data visualizations, though not explicitly.
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 guidance on when to use this tool versus alternatives like 'understand_technical_diagram' or 'analyze_image'. The description does not specify prerequisites or exclusion criteria.
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 must disclose behavioral traits. It only states the basic function without mentioning limitations (e.g., image format, size, handwritten text) or side effects. This is a significant gap for a tool with no other documentation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it omits important details. It includes useful examples but lacks structure—no sections for usage, parameters, or behavior.
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 the lack of output schema, annotations, and parameter descriptions, the tool definition is incomplete. The description does not cover return values, error conditions, or formatting, leaving the agent with insufficient context to invoke the tool correctly.
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 the description adds no information about the three parameters. The purpose of 'prompt' and 'programming_language' is unclear, and 'image_source' format is not specified. The description fails to compensate for the missing schema details.
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 extracts visible text from screenshots, listing specific content types like code and logs. This distinguishes it from sibling tools such as analyze_image or diagnose_error_screenshot, which target different tasks.
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 guidance is provided on when to use this tool versus its siblings, such as diagnose_error_screenshot or analyze_data_visualization. The description lacks context about prerequisites or preferred scenarios.
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?
No annotations are provided, so the description bears the full burden. It only states 'compare' without disclosing whether it returns a diff image, text, pass/fail, or any side effects. Lacks details on authentication, rate limits, or destructive potential.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, but it lacks structure. It does not front-load key information or use bullet points. While short, it omits critical details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without annotations, output schema, or parameter descriptions, the description is incomplete. It does not specify return format, error handling, or how the comparison is performed. For a tool comparing images, critical context is missing.
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%, meaning no parameter descriptions in schema. The tool description adds no semantics beyond parameter names. 'prompt', 'actual_image_source', and 'expected_image_source' are not explained (e.g., URL format, file type).
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 verb 'compare' and the two resources (reference and actual screenshots). It conveys the core function but does not explicitly differentiate from siblings like 'analyze_image' or 'diagnose_error_screenshot'.
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 guidance on when to use this tool versus alternatives. Does not specify prerequisites, limitations, or when not to use it.
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?
No annotations are present, so the description must fully disclose behavioral traits. It only says 'explain' without detailing how the explanation is delivered (e.g., text, structured data), what input is required beyond 'image_source' and 'prompt', or any limitations (e.g., supported image formats, resolution).
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 a single sentence that directly states the tool's purpose and lists supported diagram types. It is front-loaded and avoids redundancy, though it could be more structured by including input/output hints.
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 three parameters (two required) and no output schema or annotations, the description should explain the expected return format, prerequisites (e.g., valid image URL), or examples. It fails to do so, leaving the agent uncertain about how to invoke the tool correctly.
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%, meaning the description must compensate. It only loosely implies that 'diagram_type' corresponds to listed types, but does not explain the meaning or constraints of 'image_source' or 'prompt' (e.g., what constitutes a valid prompt). No additional parameter-level context is provided.
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 verb ('Explain') and the resource ('technical diagrams'), and enumerates specific diagram types (architecture, flowcharts, UML, ER). However, it does not explicitly differentiate from sibling tools like 'analyze_data_visualization' or 'analyze_image', which could also handle technical diagrams.
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 usage guidance is provided. The description does not specify when to use this tool versus alternatives, nor does it mention prerequisites or cases where the tool should not be used.
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?
No annotations provided, so description must carry full burden. It fails to disclose behavioral traits such as whether internet access is needed, data storage implications, or limitations (e.g., requires clear error message, cannot handle handwritten errors). The description implies a diagnostic output but lacks specifics.
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 sentence of 12 words, front-loaded with key action and outcome. No redundant information. Every word earns its place.
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 the tool has 3 parameters, no annotations, and no output schema, the description is too sparse. It does not explain return values, edge cases, or limitations. More details are needed for an agent to invoke it correctly.
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 has 3 parameters (prompt, context, image_source) with 0% description coverage. The tool description adds no meaning beyond the schema; it doesn't explain what each parameter does or how to use them. This is a significant gap given low 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 'analyze' and 'suggest', the resource 'error screenshot', and outcomes 'causes and fixes'. It distinguishes from sibling tools like 'analyze_image' (general) and 'extract_text_from_screenshot' (text-only), making it specific.
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 guidance on when to use this tool versus alternatives. Does not mention prerequisites, exclusions, or scenarios where other tools (e.g., 'analyze_image' or 'ui_diff_check') would be more appropriate.
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 does not mention how the image source is processed, what format the output takes, or any requirements/limitations (e.g., image size, type). This is insufficient for a conversion tool.
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 single-sentence description is concise and to the point. It wastes no words but could benefit from a second sentence to clarify parameter roles or output behavior without harming conciseness.
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 three required parameters, no output schema, and no annotations, the description is too brief. It omits crucial details about the output format, the meaning of each output type, and how the tool handles the image. This leaves significant gaps for an agent.
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%, so description must compensate. It only adds meaning for 'output_type' by listing the four enum values. The 'image_source' and 'prompt' parameters are not described at all, leaving ambiguity about their format or purpose.
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 converts a UI screenshot into one of four specific outputs (code guidance, recreation prompt, design spec, description). This distinct purpose sets it apart from siblings like analyze_image (general) or ui_diff_check (comparison).
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 guidance is provided on when to use this tool versus alternatives (e.g., ui_diff_check, diagnose_error_screenshot). The description lacks any context about prerequisites, limitations, or when not to use it.
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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