MuAPI Claude Code MCP Server

Server Quality Checklist

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Latest release: v1.0.1
Disambiguation5/5
Each tool targets a distinct resource and action. While generate_video and image_to_video both produce videos, one is a general generation and the other explicitly converts an image, making them clearly separable. get_prediction, account_balance, list_models, model_details, upload_file, and edit_image each serve a unique purpose.
Naming Consistency4/5
Most tools follow a verb_noun pattern (generate_image, get_prediction, list_models, edit_image, upload_file). However, account_balance and model_details are noun phrases without a verb, and image_to_video uses a noun_to_noun structure, which are minor deviations from the dominant convention.
Tool Count5/5
The server has 9 tools, which is well within the ideal 3-15 range for a domain-specific MCP server. Each tool covers a necessary aspect of MuAPI's image/video generation workflow without being redundant or excessive.
Completeness4/5
The tools cover core workflows: account access, model exploration, file upload, image generation and editing, video generation and conversion, and result retrieval. Minor gaps exist, such as no listing of past predictions or cancellation of in-progress tasks, but these are not critical for the server's primary purpose.
Average 2.9/5 across 9 of 9 tools scored. Lowest: 2.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 13 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository includes a README.md file.
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}
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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 carries the full burden of behavioral disclosure. It only states the basic conversion without revealing any behavioral traits such as whether the operation is async, requires specific model support, or has output format/quality implications. This is severely lacking for a tool with no annotation support.
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, short sentence, which is easily readable and front-loaded. However, it is under-specified to the point of being unhelpful; the conciseness does not earn its place because it omits essential context about parameters and behavior.
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 the tool's apparent complexity (image-to-video generation), the lack of annotations and output schema, and the minimal description, the overall context is critically incomplete. The agent cannot determine input formats, output expectations, or how to handle 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?
The input schema has two required parameters (image and prompt) but the schema provides no descriptions (0% coverage). The description does not mention either parameter, so the agent receives no guidance on what formats, meanings, or constraints apply to these fields.
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 uses the specific verb 'convert' and clearly identifies the resource being transformed (image) and the output (video). It is clear about the tool's core function, though it does not explicitly differentiate from sibling tools like generate_video or generate_image.
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 such as generate_video or edit_image. There is no mention of prerequisites, intended use cases, or scenarios where a different tool 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.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for disclosing behavior, but it only restates the tool's purpose. It omits information about return values, side effects, authentication requirements, or limitations.
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 very short and front-loaded, but it essentially repeats the tool name. It is not as under-specified as 'Process', yet it lacks useful context that could be conveyed in the same length.
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 annotations and no output schema, the description still fails to explain editing behavior, expected output, or operational constraints. The schema covers parameters, but overall completeness is poor for a tool among media generation siblings.
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?
Both parameters are fully described in the schema ('Image URL' and 'Edit instruction'), achieving 100% coverage. The description adds no additional parameter context, so baseline 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 uses a specific verb ('Edit') with a clear resource ('an image'), distinguishing it from sibling tools like generate_image and image_to_video. However, it is minimal and doesn't elaborate on the editing scope or capabilities.
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. There is no mention of prerequisites, specific use cases, or exclusions. The only implicit signal is the action verb 'edit'.
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 available, so the description carries the full burden of disclosing behavioral traits. It mentions the action 'upload' but does not disclose side effects, authentication needs, file size limits, or response 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 sentence, front-loaded, and free of redundancy. It is appropriately concise for a simple tool.
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?
The description is incomplete for a tool with no output schema and no annotations. It does not mention return values, failure modes, supported file types, or any context about how the file is uploaded, leaving the agent without crucial 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 schema has one string parameter 'filepath' with no description. The tool description adds no explanation of the parameter's meaning, format, or constraints. With 0% schema description coverage, the description fails to compensate.
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 'Upload a file to MuAPI' with a specific verb and resource. It is unambiguous about the action, though it does not explicitly differentiate from sibling tools; however, 'upload' is unique among the listed siblings.
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, prerequisites, or exclusions. The description simply states the action without any contextual cues for appropriate use.
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 the full burden of disclosing behavior, but it only says 'get details'. It does not mention whether the operation is read-only, what the response structure is, or any potential errors or limitations, offering minimal transparency.
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, front-loaded sentence with no extraneous words, achieving good conciseness. However, it is so minimal that it borders on under-specification, which prevents a perfect score.
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 and an output schema, the description is too sparse to be complete. It fails to provide details about return values, how to interact with the model parameter, or any connection to sibling tools, leaving significant gaps for the 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 description coverage is 0%, so the description must compensate by explaining the 'model' parameter, but it only repeats the parameter name without specifying the expected format, valid values, or how it identifies the model. The word 'specific' adds little beyond the schema.
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 gets details about a model, using the verb 'get' and resource 'model'. The modifier 'specific' implies it operates on a single model, which differentiates it from the sibling tool 'list_models', though it does not explicitly name it.
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 like list_models. The description does not mention any preconditions, intended scenarios, or exclusions, leaving the agent without context for tool 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 the full burden but only states the basic operation. It does not disclose whether the operation is asynchronous, whether it returns a prediction that must be polled (given the get_prediction sibling), or any resource/rate limit implications.
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, terse sentence with no wasted words and is front-loaded with the action verb. However, it is so brief that it omits potentially important context, though that is a completeness issue rather than a conciseness issue.
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?
The description lacks return value information, asynchronous behavior, and any relationship to the get_prediction tool. Given no output schema and no annotations, the description is insufficient for an agent to understand the full flow of generating an image.
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 covers the only parameter 'prompt' with a description, so the 100% coverage baseline applies. The tool description itself adds no additional parameter context beyond the schema.
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 action ('Generate an image') and platform ('MuAPI'), using a specific verb and resource. However, it does not differentiate from sibling tools like edit_image or generate_video, so it falls short of full clarity.
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 the sibling tools (generate_video, edit_image, image_to_video). There is no mention of suitable use cases, prerequisites, or alternatives.
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?
Without annotations, the description carries the full burden for behavioral disclosure. It only states 'Generate a video', implying a creation action, but gives no details about whether it is synchronous, requires an API key, costs credits, returns a video URL or job ID, or has rate limits. This is a significant gap for a generation tool.
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 one concise sentence, front-loaded with the core action, and contains no fluff. It is appropriately sized for a simple tool with a single parameter.
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?
The description is minimal and omits essential context for a generation tool with no output schema. Given sibling 'get_prediction' suggests an async workflow, the description should at least mention whether the tool returns a direct video or a job identifier to be polled. As-is, an agent lacks enough context to use the tool correctly.
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 covers the single 'prompt' parameter with description 'Video generation prompt', achieving 100% schema coverage. The description adds no additional parameter context, but the baseline for full schema coverage is 3, and the parameter is self-explanatory.
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 using MuAPI' with a specific verb and resource. However, it does not differentiate from sibling tool 'image_to_video', which also generates video, and lacks scope details (e.g., text-to-video).
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 image_to_video or generate_image. It does not mention prerequisites, workflows, or any exclusions, leaving usage decisions entirely to the agent.
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 the full burden of behavioral disclosure. It only states the basic action and does not mention error handling, side effects, or what happens if the request_id is invalid.
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, front-loaded sentence with no filler. Every word contributes meaning, making it highly concise.
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?
The tool is simple, but the description is minimal. Without annotations or output schema, the agent lacks information about error behavior, usage context, or expected response, leaving important gaps.
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 has one parameter with 0% description coverage. The description adds that request_id is used to fetch the result, providing some semantic context, but lacks details about format or provenance.
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 retrieves a prediction result using a request ID, using a specific verb and resource. It does not explicitly contrast with sibling tools, but the function is distinct enough that it likely stands alone.
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, nor any prerequisites like how to obtain a request_id. The agent is left 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states 'Get MuAPI account balance' without mentioning authentication, return format, or read-only assurance, which is a significant gap for an API tool.
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 concise sentence that is easy to parse and front-loads the key information. It earns its place without any waste, making it an example of efficient specification.
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 lack of an output schema, the description should clarify what the agent will receive (e.g., balance value, currency, JSON structure). It merely says 'account balance', which is minimally adequate but leaves room for interpretation about response details and any access prerequisites.
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 tool has zero parameters, and the schema already covers everything. The description correctly omits parameter details, matching the baseline of 4 for tools with no parameters.
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 uses a specific verb 'Get' and specific resource 'MuAPI account balance', clearly identifying the tool's function. It is distinct from sibling tools that generate or predict content. However, it lacks a brief elaboration on the scope or purpose of the balance (e.g., credit balance, usage limit), which would fully distinguish it from potential overlap.
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 gives no context about typical use cases, prerequisites, or scenarios where this tool is preferable, leaving the agent to infer usage from the tool name 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the list action, with no mention of pagination, authentication requirements, scope of 'available', or return format. This is minimal 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 a single concise sentence with no superfluous words. It front-loads the action and resource, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter list tool, the description is reasonably complete. However, it could be enhanced by clarifying what 'categories' means or explicitly referencing that more detailed information is available via sibling tools like 'model_details'.
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 tool has zero parameters, so the schema fully covers parameter semantics by being empty. A baseline of 4 is appropriate since there are no parameter details to document.
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 uses a specific verb 'List' and identifies a clear resource: 'available MuAPI model categories'. This distinguishes it from sibling tools like 'model_details' that typically target individual models.
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?
Usage is implied as a read-only listing operation, but the description provides no explicit when-to-use guidance or exclusions. The sibling context suggests alternatives like 'model_details' exist, but the description doesn't clarify how this tool fits relative to them.
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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