ImaginePro MCP Server

Server Quality Checklist

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Latest release: v1.1.2
Disambiguation4/5
Most tools have distinct purposes, but 'gemini-imagine' and 'generate-image' could cause confusion as both generate images from text prompts. The other tools target clearly different operations like upscaling, inpainting, and video generation.
Naming Consistency3/5
The naming is mixed with hyphenated and underscore styles, and verbs vary (e.g., 'create-variant' vs. 'generate-image'). However, all names are readable and follow a general verb-noun pattern, though not uniformly applied.
Tool Count5/5
With 8 tools, this is well-scoped for an image/video generation server. Each tool addresses a specific need in the creative workflow, from generation to editing and status checking, without being overwhelming.
Completeness4/5
The toolset covers core operations like generation, editing, upscaling, and status tracking. A minor gap is the lack of a tool for deleting or managing generated assets, but the provided tools support most common workflows effectively.
Average 3.1/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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 generates image variants but doesn't explain what 'alternative versions' means (e.g., style variations, size changes, content edits), whether this is a read-only or mutating operation, or any performance characteristics like rate limits or processing time.
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 without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 tool has an output schema (which handles return values) and 100% schema coverage for parameters, the description's minimal approach is somewhat adequate. However, for a tool that likely involves image processing with behavioral nuances, more context about what 'alternative versions' entails would improve 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 100%, so the schema already documents all three parameters thoroughly. The description adds no additional meaning about parameters beyond implying 'messageId' refers to an existing image. This meets the baseline of 3 when the schema does the heavy lifting.
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 alternative versions') and resource ('of an existing image'), making the purpose immediately understandable. However, it doesn't distinguish this from sibling tools like 'reroll-image' or 'inpaint-image' that might also create variations, missing an opportunity for clearer differentiation.
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 alternatives like 'reroll-image' or 'inpaint-image'. It doesn't mention prerequisites (e.g., needing an existing image via 'messageId') or contextual constraints, leaving the agent to infer usage from the tool name and parameters alone.
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 states the tool generates images but doesn't mention any behavioral traits such as rate limits, authentication needs, output format, or potential side effects (e.g., whether it modifies input images). This leaves significant gaps in understanding how the tool behaves beyond its basic 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 a single, efficient sentence that front-loads the core purpose ('Generate images') and specifies the input method. There is no wasted language or unnecessary elaboration, making it highly concise and well-structured for quick understanding.
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 tool's complexity (multi-modal image generation with 4 parameters) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and incomplete behavioral context, it doesn't fully prepare an agent for safe and effective use, especially compared to sibling tools that might have overlapping functions.
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 parameters thoroughly. The description adds no additional meaning beyond implying that 'contents' can include both text and images, which is already clear from the schema's enum and descriptions. This meets the baseline for high schema coverage without extra value.
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 images') and the input modality ('using multi-modal inputs (text + images)'), which distinguishes it from text-only generation tools. However, it doesn't explicitly differentiate from sibling tools like 'generate-image' or 'inpaint-image' that might also create images, leaving some ambiguity about when to choose this specific tool.
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 alternatives like 'generate-image' or 'inpaint-image' from the sibling list. It mentions multi-modal inputs but doesn't specify scenarios where text+image inputs are preferred over text-only or other methods, offering no usage context or exclusions.
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 the full burden of behavioral disclosure. It mentions the core action ('Generate an AI image') but lacks details on permissions, rate limits, costs, async behavior (implied by 'webhookOverride' but not explained), or output format. For a generative tool with potential side effects, this is a significant gap 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, efficient sentence: 'Generate an AI image from a text prompt using ImaginePro.' It's front-loaded with the core purpose, has zero waste, and is appropriately sized for the tool's complexity. Every word 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 the tool has an output schema (which handles return values), 100% schema coverage for inputs, and no annotations, the description is minimally adequate. However, it lacks context on behavioral traits (e.g., async processing, costs) and sibling differentiation, making it incomplete for optimal agent use despite the structured data support.
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 three parameters ('prompt', 'ref', 'webhookOverride') with clear descriptions. The description adds no additional semantic context beyond implying the tool uses a 'text prompt,' which is redundant with the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
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: 'Generate an AI image from a text prompt using ImaginePro.' It specifies the verb ('Generate'), resource ('AI image'), and mechanism ('from a text prompt using ImaginePro'). However, it doesn't explicitly differentiate from siblings like 'gemini-imagine' or 'create-variant,' which might offer similar functionality.
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 alternatives. With siblings like 'gemini-imagine' (possibly another image generator), 'create-variant' (for modifying images), and 'reroll-image' (for regenerating), there's no indication of when this specific tool is preferred or what distinguishes it from others in the server.
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 the full burden of behavioral disclosure. It mentions generating a video animation but fails to describe critical traits: whether this is a long-running process, if it requires authentication, rate limits, output format, or error handling. For a tool with 5 parameters and no annotations, this is a significant gap 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 extremely concise—a single sentence that directly states the tool's core function. It's front-loaded with no wasted words, making it easy to parse quickly. Every part of the sentence contributes to understanding the purpose.
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 tool's complexity (5 parameters, no annotations, but with an output schema), the description is minimally adequate. The output schema likely covers return values, reducing the need for that in the description. However, for a video generation tool with no behavioral context, the description should do more to explain process characteristics and usage scenarios to be fully complete.
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 description implies parameters like 'start and end frame images' but doesn't add meaningful semantics beyond what the schema provides. With 100% schema description coverage, the schema already documents all 5 parameters thoroughly. The description doesn't explain relationships between parameters (e.g., how 'prompt' relates to the frames) or usage nuances, so it meets the baseline without adding extra value.
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: 'Generate a video animation from start and end frame images.' This specifies the action (generate), resource (video animation), and key inputs (frame images). However, it doesn't differentiate from sibling tools like 'generate-image' or 'create-variant' beyond mentioning 'video animation,' leaving some ambiguity about its unique role.
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 alternatives. With siblings like 'generate-image' and 'create-variant' available, there's no indication of scenarios where this tool is preferred, prerequisites, or exclusions. This lack of context makes it harder for an agent to choose appropriately.
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 full burden for behavioral disclosure. While 'Edit' implies mutation, it doesn't specify what kind of editing occurs, whether it's destructive to the original image, what permissions are required, or how the editing process works. The description lacks critical behavioral context for an image editing operation.
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 at just 8 words, front-loading the core functionality with zero wasted words. Every element ('Edit specific regions', 'image', 'using a mask') contributes essential information about the tool's purpose and method.
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 that there's an output schema (which handles return values) and 100% schema description coverage, the description is reasonably complete for its length. However, for an image editing tool with no annotations and multiple sibling tools, it should provide more context about when to use it and what the editing entails.
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 5 parameters thoroughly. The description adds minimal value beyond what's in the schema - it mentions 'mask' which relates to 'maskUrl' and 'specific regions' which relates to the editing concept, but doesn't provide additional semantic context about parameter relationships or usage patterns.
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 ('Edit specific regions') and resource ('image') using a specific method ('using a mask'). It distinguishes from siblings like 'create-variant' or 'generate-image' by focusing on editing existing images with masks rather than creating new ones. However, it doesn't explicitly differentiate from 'reroll-image' or 'upscale-image' which might also involve image modification.
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 alternatives. With siblings like 'create-variant', 'reroll-image', and 'upscale-image' that might also modify images, there's no indication of when inpaint-image is appropriate versus those other tools. No prerequisites or exclusions are mentioned.
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 full burden but only states the basic action. It doesn't disclose behavioral traits such as whether this is a read/write operation, rate limits, authentication needs, or what 'regenerate' entails (e.g., creates a new image, modifies existing). This leaves significant gaps for a tool that likely involves mutation.
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 with zero waste. It's front-loaded and appropriately sized for the tool's complexity, making it easy to parse quickly.
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 tool has an output schema (which handles return values) and 100% schema coverage, the description is minimally adequate. However, for a mutation tool with no annotations, it should provide more behavioral context (e.g., side effects, permissions) to be fully complete, leaving room for improvement.
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 parameters. The description adds no meaning beyond the schema, as it doesn't explain how 'messageId' relates to the image or what 'reroll' implies for parameters. Baseline 3 is appropriate since the schema does the heavy lifting.
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 ('Regenerate') and resource ('image'), specifying it uses the same prompt. However, it doesn't differentiate from sibling tools like 'create-variant' or 'generate-image', which might have overlapping functionality.
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 alternatives like 'create-variant' or 'generate-image'. It lacks context about prerequisites (e.g., needing an existing image) or exclusions, leaving the agent to infer usage.
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 'enhance resolution and quality' but lacks details on how the enhancement works (e.g., AI upscaling, interpolation), potential side effects (e.g., file size increase, quality loss), rate limits, or authentication needs. This is inadequate for a tool that likely performs a non-trivial operation.
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 with no wasted words. It is front-loaded with the core purpose, making it easy to scan and understand quickly.
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 tool's moderate complexity (image processing), no annotations, and an existing output schema (which handles return values), the description is minimally adequate. However, it lacks context on behavioral aspects like how enhancement is performed or error conditions, leaving gaps for an AI agent to fully understand the tool's operation.
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 input schema has 100% description coverage, so parameters are well-documented in the schema itself. The description adds no additional meaning beyond implying that 'messageId' refers to an image, which is already clear from the schema. This meets the baseline for high schema coverage.
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 'Enhance image resolution and quality' clearly states the tool's function with specific verbs ('enhance') and resource ('image'), distinguishing it from siblings like 'inpaint-image' or 'generate-image'. However, it doesn't explicitly differentiate from 'create-variant' or 'reroll-image', which might also involve image modifications, keeping it from a perfect score.
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 alternatives like 'create-variant' or 'reroll-image', nor does it mention prerequisites such as needing an existing image message ID. It only states what the tool does, not when or why to invoke 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 provided, so the description carries the full burden of behavioral disclosure. It states the action ('Check the status') but does not describe traits like whether this is a read-only operation, potential rate limits, error conditions, or what the status response includes (e.g., pending, completed, failed). This leaves significant gaps for an agent to understand the tool's behavior.
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, clear sentence that efficiently conveys the core purpose without unnecessary words. It is front-loaded and appropriately sized for the tool's functionality, with no wasted information.
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 moderate complexity (a status check with one parameter) and the presence of an output schema (which handles return values), the description is reasonably complete. However, it lacks behavioral details (e.g., read-only nature, error handling) that would be beneficial for an agent, especially without annotations, slightly reducing 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?
The input schema has 100% description coverage, with the 'messageId' parameter documented as 'Message ID to check status for'. The description does not add any additional meaning beyond this, such as explaining where to obtain the messageId or its format. Given the high schema coverage, a baseline score 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 verb ('Check') and resource ('status of an image or video generation task'), making the purpose specific and understandable. However, it does not explicitly differentiate from potential sibling tools like 'create-variant' or 'reroll-image' that might also involve status checking, though those appear to be creation tools rather than status queries.
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 alternatives. It does not mention prerequisites (e.g., needing a messageId from a prior generation task) or specify scenarios where this is the appropriate choice among the sibling tools, such as 'generate-image' for new tasks.
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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