Image MCP Server

Server Quality Checklist

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Latest release: v0.0.13
Disambiguation2/5
Multiple tools have unclear boundaries and overlapping purposes. The 'landscape_image', 'portrait_image', and 'square_image' tools are explicitly described as shortcuts for 'generate_ai_image', creating significant ambiguity about when to use the general tool versus the specific ones. Additionally, 'pizza-test' appears unrelated to image generation, further confusing the tool set's purpose.
Naming Consistency3/5
The naming conventions are mixed but still readable. Most tools follow a consistent snake_case pattern (e.g., 'generate_ai_image', 'landscape_image'), but 'pizza-test' uses kebab-case, breaking the pattern. The verb styles are somewhat inconsistent, with 'generate' used for some and implied for others.
Tool Count4/5
The tool count of 5 is reasonable for an image generation server, though it feels slightly thin due to the redundancy among tools. It's not excessive, but the inclusion of a mock test tool ('pizza-test') that doesn't align with the domain reduces the effective scope, making the count borderline appropriate.
Completeness2/5
There are significant gaps in the tool surface for an image generation domain. The server lacks essential operations such as editing, resizing, filtering, or deleting images, and it offers no way to manage or retrieve generated images. The tools are narrowly focused on generation only, with redundant shortcuts, and the unrelated 'pizza-test' tool adds confusion rather than functionality.
Average 3.4/5 across 5 of 5 tools scored. Lowest: 2.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No critical vulnerability alerts
- No high-severity vulnerability alerts
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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 carries the full burden of behavioral disclosure. It states the tool returns a password, which implies it's a read operation, but doesn't clarify if this is a mock/dummy password, if it has security implications, or what the return format is. The 'mock test' hint suggests it's for testing, but this is insufficient for a tool with no annotation coverage.
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 function. It's front-loaded and wastes no words, though it could be slightly more informative. For a simple tool, this is appropriately concise and structured.
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 no annotations, no output schema, and a simple purpose, the description is incomplete. It doesn't explain what 'mock test' means, the nature of the returned password, or how this fits with sibling tools. For a tool that returns sensitive-sounding data (a password), more context is needed despite the low complexity.
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?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, but that's acceptable here. Baseline is 4 for 0 parameters, as the schema fully covers the absence of inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Mock test tool that returns a password' states a purpose but is vague about what 'test' entails and doesn't specify the resource or context. It's not tautological (doesn't just restate 'pizza-test'), but it's too generic to be clear about the tool's specific function beyond returning a password.
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 use this tool versus its siblings (e.g., image generation tools). It mentions it's a 'test' tool, which implies limited or debugging use, but doesn't explicitly state when or why an agent should choose it over alternatives, leaving usage unclear.
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 provided, the description carries the full burden of behavioral disclosure. It mentions the implementation module ('ai-image npm module') but doesn't describe key behavioral traits such as whether this is a read-only or mutation operation, what permissions or authentication might be required, rate limits, error handling, or what the output looks like (e.g., file saved locally, URL returned). For a tool with 7 parameters and no annotations, this is a significant gap.
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, efficient sentence that directly states the tool's purpose and implementation method without any unnecessary words or structural issues. It's appropriately sized and front-loaded, with every part earning 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 complexity (7 parameters, no annotations, no output schema), the description is incomplete. It doesn't address behavioral aspects like mutation effects, authentication needs, or output format, and it lacks usage guidance relative to sibling tools. For a tool with this level of complexity and no structured support, the description should provide more context to be fully helpful.
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?
The schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain parameter interactions, default behaviors, or constraints not captured in schema descriptions). According to the rules, with high schema coverage (>80%), the baseline is 3 even with no param info in the description.
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 ('Generate AI images') and specifies the implementation method ('using the ai-image npm module'), which provides a specific verb+resource combination. However, it doesn't distinguish this tool from its sibling tools (landscape_image, portrait_image, square_image), which likely serve similar image generation purposes but with different constraints or outputs.
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 use this tool versus its siblings or alternative approaches. It mentions the implementation module but doesn't explain the context, prerequisites, or exclusions for using this tool, leaving the agent with no usage direction beyond the basic function stated.
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 carries full burden. While it mentions the tool generates images and works for cover images, it lacks critical behavioral details: whether this is a read-only or write operation, what permissions are needed, whether it makes external API calls, rate limits, or what happens on failure. For a generative AI tool with no annotation coverage, this is a significant gap.
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 with just two short sentences that pack essential information: tool function, dimensions, use case, and sibling relationship. Every word earns its place with zero waste, making it front-loaded and easy to parse.
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 complexity of an AI image generation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., image file path, success status), error conditions, or behavioral constraints. While purpose and usage are clear, critical operational context is missing for a tool that likely involves external services and file system operations.
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 fully documents all 6 parameters. The description adds no parameter-specific information beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description, which applies here.
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 with specific verb ('Generate') and resource ('landscape AI image'), including exact dimensions (1536x1024) and use case ('works great for cover images'). It explicitly distinguishes from sibling 'generate_ai_image' by calling itself a 'shortcut' for that tool, providing clear differentiation.
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?
The description provides explicit usage guidance by stating this is a 'shortcut for generate_ai_image,' indicating when to use this tool versus its sibling. It also implies context with 'works great for cover images,' suggesting appropriate use cases. No misleading guidance is present.
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 carries full burden. While 'Generate' implies creation, it lacks critical behavioral details: it doesn't mention whether this is a read-only or destructive operation, what permissions are needed, rate limits, or what happens on failure. The description adds minimal behavioral context beyond the basic action.
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 with just two clauses, front-loading the core purpose and following with the sibling relationship. Every word earns its place with zero wasted text, making it highly efficient for agent comprehension.
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 annotations and no output schema, the description is incomplete for a generative AI tool. It doesn't explain what the tool returns (e.g., image file, URL, metadata), error conditions, or behavioral constraints. While concise, it leaves significant gaps in understanding the tool's full operation and results.
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 fully documents all 6 parameters. The description adds no parameter-specific information beyond implying portrait dimensions (1024x1536), which isn't directly tied to any parameter in the schema. Baseline 3 is appropriate when the schema does all the parameter documentation work.
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 specific action ('Generate'), resource ('portrait AI image'), and dimensions ('1024x1536'), distinguishing it from sibling tools like landscape_image and square_image by specifying portrait orientation. It also explicitly identifies it as a shortcut for generate_ai_image, providing clear differentiation.
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?
The description explicitly states when to use this tool ('shortcut for generate_ai_image'), implying it should be used specifically for portrait images rather than the more general generate_ai_image or other sibling tools like landscape_image. This provides clear alternative guidance.
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 carries full burden. While it mentions the fixed 1024x1024 output size, it doesn't disclose important behavioral aspects like whether this is a read-only or write operation, potential rate limits, authentication requirements, or what happens when the image is generated (e.g., where it's saved, format, etc.). For a tool that presumably creates files, this is insufficient behavioral disclosure.
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 (one sentence) and front-loaded with the core purpose. Every word earns its place by specifying dimensions and providing the sibling tool comparison. No wasted words or redundancy.
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 annotations and no output schema, the description should do more to explain what this tool returns or produces. While it mentions the fixed square dimensions, it doesn't describe the output format, file location, or what happens after generation. For an image generation tool with 6 parameters, this leaves significant gaps in understanding the tool's complete 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%, so the schema already documents all 6 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. This meets the baseline expectation when schema coverage is complete.
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 specific action ('Generate a square AI image') with precise dimensions (1024x1024) and explicitly distinguishes it from its sibling tool 'generate_ai_image' by calling it a 'shortcut'. This provides excellent differentiation and clarity.
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?
The description explicitly positions this tool as a 'shortcut for generate_ai_image', providing clear guidance on when to use this tool versus its more general sibling. This direct comparison to an alternative tool is optimal for usage guidance.
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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