MiniMax MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes (video generation, music generation, text-to-audio, text-to-image, voice cloning/design), but there is some overlap between list_voices and voice_clone/voice_design regarding voice management, and play_audio is a generic utility that could be confused with audio output from other tools. The descriptions help clarify, but the boundaries are not perfectly sharp.
Naming Consistency5/5
All tool names follow a consistent snake_case verb_noun pattern (e.g., generate_video, list_voices, music_generation, query_video_generation). The naming is predictable and readable throughout, with no deviations in style or convention.
Tool Count5/5
With 9 tools, the count is well-scoped for a multimedia generation server covering video, audio, image, and voice functionalities. Each tool serves a clear purpose, and there are no extraneous or missing tools that would make the set feel too heavy or thin for the domain.
Completeness4/5
The tool set covers core multimedia generation tasks (text-to-video, text-to-audio, text-to-image, music generation, voice cloning/design) with good lifecycle support (e.g., async video generation with query_video_generation). Minor gaps include no direct tools for editing or manipulating generated media, and play_audio is a utility that doesn't fully integrate with the generation workflow, but agents can work around these limitations.
Average 3.9/5 across 9 of 9 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
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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

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 lacks behavioral details. It states what the tool does (play audio) and returns (text with path), but doesn't disclose critical traits like whether it requires specific permissions, if it blocks until playback completes, error handling, or system resource implications. The return value description is minimal and doesn't explain what 'text content with the path' means in practice.
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 appropriately sized and front-loaded with the core purpose in the first sentence. The Args/Returns sections are structured but could be more integrated. Some redundancy exists (e.g., 'Not supports video' could be merged with format support), but overall it's efficient with minimal waste.
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, no output schema, and 0% schema coverage, the description is incomplete for a tool with 2 parameters. It covers basic functionality and parameters but misses behavioral context (e.g., playback controls, error conditions), detailed return format, and integration guidelines. For an audio playback tool, this leaves significant gaps in understanding how to use it effectively.
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 0%, so the description must compensate. It adds some meaning: it explains 'input_file_path' as 'the path to the audio file to play' and mentions 'is_url' indicates 'whether the audio file is a URL'. However, it doesn't clarify path format expectations (absolute/relative), URL validation, or default behavior when is_url is false. This provides basic but incomplete parameter context.
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: 'Play an audio file' with specific format support (WAV and MP3) and exclusion of video. It distinguishes from siblings like 'text_to_audio' or 'music_generation' by focusing on playback rather than generation. However, it doesn't explicitly differentiate from all siblings (e.g., 'voice_clone' might also involve audio).
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 is provided. The description mentions format support and URL capability but doesn't explain when to choose this over siblings like 'text_to_audio' for audio creation or 'music_generation' for music playback. There's no mention of prerequisites or typical use cases.
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. It states the tool lists voices and describes the return format ('Text content with the list of voices'), but lacks critical behavioral details: whether this is a read-only operation, if it requires authentication, rate limits, pagination behavior, or error conditions. For a tool with zero annotation coverage, this is insufficient.
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 appropriately sized and well-structured with clear sections for Args and Returns. The opening sentence states the core purpose, followed by organized parameter documentation. While efficient, the 'Returns' section could be slightly more specific about the format of the voice list.
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 low complexity (1 optional parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameter semantics well, but lacks behavioral context that would be important for reliable tool invocation. The absence of output schema means the description should ideally provide more detail about return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant value beyond the input schema, which has 0% description coverage. It fully documents the single parameter 'voice_type', including its optional nature, valid values (['all', 'system', 'voice_cloning']), and default value ('all'). This completely compensates for the schema's lack of parameter documentation.
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 with 'List all voices available' - a specific verb ('List') and resource ('voices'). It distinguishes itself from siblings like 'voice_clone' or 'voice_design' by focusing on listing rather than creating or modifying voices. However, it doesn't explicitly differentiate from other listing tools since none exist among 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?
The description provides no guidance on when to use this tool versus alternatives. While the purpose is clear, there's no mention of prerequisites, when this tool is appropriate versus other voice-related tools like 'voice_clone' or 'voice_design', or any context about typical use cases. The agent must 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 mentions that the tool queries status and saves output to a directory, but it doesn't cover critical aspects like authentication needs, rate limits, error handling, or what the status response includes (e.g., pending, completed, failed). This is a significant gap for a tool with potential async operations.
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 appropriately sized and front-loaded, starting with the core purpose. The structured 'Args' and 'Returns' sections add clarity without redundancy. However, the inclusion of 'Returns' details could be slightly verbose since there's no output schema, but it's still efficient overall.
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 complexity of async video generation, no annotations, and no output schema, the description is moderately complete. It covers the basic purpose and parameters but lacks details on behavioral traits (e.g., what the status text contains, error cases). The return statement is helpful but vague, leaving room for improvement in fully guiding the agent.
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 description adds meaningful context beyond the input schema, which has 0% description coverage. It explains that 'task_id' should come from 'generate_video' if 'async_mode' is True and that 'output_directory' is for saving the video. This clarifies usage and dependencies, compensating well for the schema's lack of descriptions, though it doesn't detail parameter formats or constraints.
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: 'Query the status of a video generation task.' It specifies the verb 'query' and the resource 'video generation task,' making the intent unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'generate_video' beyond mentioning it in the parameter description, which is helpful but not a direct comparison.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by referencing the 'generate_video' tool in the parameter description, suggesting it should be used when 'async_mode' is True. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., for synchronous tasks or other status-checking methods) and doesn't mention prerequisites or exclusions, leaving some context gaps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses critical behavioral traits: cost implications ('charged upon first use', 'may incur costs'), and the return format ('voice id of the cloned voice'). However, it doesn't mention rate limits, authentication requirements, error conditions, or what happens if the voice_id already exists.
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 appropriately sized and well-structured with purpose statement, cost warning, parameter explanations, and return value - all in logical order. Every sentence adds value. Minor improvement could be front-loading the return information more prominently.
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?
For a 5-parameter mutation tool with no annotations and no output schema, the description does reasonably well covering parameters and basic behavior. However, it lacks details about error handling, rate limits, authentication needs, and what constitutes valid audio files. The cost warning is helpful but doesn't specify magnitude or billing 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?
With 0% schema description coverage, the description compensates well by explaining all 5 parameters in the Args section. It clarifies that 'file' can be a path or URL, 'is_url' defaults to False, 'text' is for demo audio, and 'output_directory' is for saving. The only gap is not explaining the purpose of 'voice_id' beyond 'id of the voice to use'.
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 ('Clone a voice') and resource ('using provided audio files'), making the purpose immediately understandable. It distinguishes from siblings like 'list_voices' or 'voice_design' by specifying creation rather than listing or designing. However, it doesn't explicitly contrast with all possible alternatives like 'text_to_audio'.
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 provides clear context about when to use ('Only use when explicitly requested by the user') and includes a cost warning that helps determine appropriateness. It doesn't explicitly name alternative tools or specify when NOT to use beyond the cost consideration, but the guidance is practical and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 does well by warning about API costs and specifying that it saves files to an output directory. However, it doesn't mention other important behavioral aspects like rate limits, error handling, or whether the operation is idempotent. The cost warning and file-saving behavior are valuable additions beyond basic functionality.
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 well-structured with clear sections: purpose statement, cost warning, parameters, and return value. Each sentence earns its place, though the parameter documentation is quite detailed (which is necessary given the poor schema coverage). The front-loaded purpose statement and cost warning are particularly effective.
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 complexity (6 parameters, no annotations, no output schema), the description does a good job of providing necessary context. It explains parameters thoroughly, warns about costs, and describes the return value. However, it could benefit from more behavioral context (like error conditions or performance characteristics) to be fully complete for a tool with external API dependencies.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates excellently by providing detailed parameter information in the Args section. It explains each parameter's purpose, valid value ranges, and default values. This adds significant meaning beyond what the bare schema provides, making it easy for an AI agent to understand how to use each parameter correctly.
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 image from a prompt.' This specifies the verb ('generate') and resource ('image'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'generate_video' or 'text_to_audio' beyond the obvious image vs. audio/video distinction.
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 provides clear usage guidance with the 'COST WARNING' section, explicitly stating when to use it ('Only use when explicitly requested by the user') and warning about API costs. This is helpful context for an AI agent. However, it doesn't mention when to choose this tool over alternatives like 'generate_video' for visual content generation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 effectively describes key behavioral traits: it's a generative tool that makes an external API call (to Minimax), incurs potential costs, and outputs a file path. However, it doesn't mention rate limits, error handling, or authentication needs, leaving some gaps.
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 appropriately sized and front-loaded: the first sentence states the purpose, followed by cost warning and parameter details. Every sentence adds value, with no wasted words. Minor points deducted for slightly dense parameter formatting, but overall it's efficient.
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 complexity (external API, file output) and lack of annotations/output schema, the description does a good job covering essentials: purpose, usage warning, parameters, and return value. It could improve by mentioning file formats, error cases, or sibling tool relationships, but it's largely complete for agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics for all parameters: 'prompt' is for voice generation, 'preview_text' is for voice previewing, 'voice_id' includes example values, and 'output_directory' specifies where to save. This goes well beyond the bare schema, though it could provide more detail on format constraints.
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 voice based on description prompts.' It specifies the verb ('Generate') and resource ('a voice'), making the function immediately understandable. However, it doesn't differentiate from sibling tools like 'voice_clone' or 'text_to_audio,' which prevents 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 Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Only use when explicitly requested by the user.' This clearly defines when to use the tool (user request) and implies when not to use it (without explicit request). The cost warning further reinforces prudent usage, making this excellent guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and delivers substantial behavioral context. It discloses cost implications (API call to Minimax with potential costs), async mode behavior (returns task_id, requires separate query tool), and output format (path to video file). It could improve by mentioning rate limits or error handling.
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 appropriately front-loaded with core purpose and critical warnings, but the parameter documentation is verbose with detailed enum values and camera instructions that might belong better in schema descriptions. Some sentences could be more concise while maintaining clarity.
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 complex 7-parameter tool with no annotations and no output schema, the description provides substantial context including behavioral traits, parameter semantics, and usage constraints. It explains the return value format. Minor gaps include lack of error conditions, rate limits, or authentication requirements.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for 7 parameters, the description fully compensates by providing rich semantic details for all parameters. It explains model types and their purposes (T2V, I2V, Director variants), prompt constraints with camera instructions, parameter dependencies (first_frame_image requires I2V models, duration/resolution require MiniMax-Hailuo-02), and async_mode implications.
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 a specific verb ('generate') and resource ('video from a prompt'), distinguishing it from sibling tools like text_to_image or text_to_audio. It explicitly identifies the core functionality of video generation from textual or image inputs.
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 provides clear usage context with the cost warning and explicit instruction to 'only use when explicitly requested by the user.' It mentions the alternative tool 'query_video_generation' for async mode but doesn't differentiate from other video-related siblings (none exist) or explain when to choose specific models beyond their capabilities.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 effectively describes key behaviors: it's a mutation tool (creates/saves files), includes a cost warning for API calls to Minimax, specifies default behaviors (directory, voice, parameters), and mentions the return format. It doesn't cover rate limits or error handling, but provides substantial operational context.
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 appropriately front-loaded with the core purpose and key optional parameters, but becomes verbose with repetitive phrasing ('Controls the... of the generated speech') for each audio parameter. While informative, this repetition reduces efficiency. The structure is logical but could be more streamlined.
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 complexity (13 parameters, mutation operation, no annotations, no output schema), the description is largely complete. It covers purpose, usage warning, parameter details, and return information. Minor gaps include lack of error scenarios or file naming conventions, but it provides sufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must fully compensate. It does so excellently by providing detailed semantics for all 13 parameters: purpose, optionality, default values, value ranges, and examples. This goes far beyond what the bare schema provides, making parameters fully understandable.
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 verbs ('convert text to audio', 'save the output audio file') and resources ('text', 'audio file', 'directory'). It distinguishes from sibling tools like 'play_audio' (which plays rather than creates) and 'voice_clone' (which clones rather than converts text).
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 provides clear context for usage with the COST WARNING section ('Only use when explicitly requested by the user'), which helps guide when to use it. However, it doesn't explicitly mention when NOT to use it or name specific alternatives among siblings (e.g., 'voice_design' or 'music_generation'), though the purpose distinction is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing cost implications, API provider (Minimax), length limitation ('Currently supports generating music up to 1 minute in length'), and output behavior ('Text content with the path to the generated music file or generation status'). It could improve by mentioning authentication needs or rate limits, but covers key behavioral aspects.
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 well-structured with clear sections (purpose, warning, parameters, notes, returns) and front-loaded information. Most sentences earn their place, though the parameter explanations are quite detailed, which is necessary given the lack of schema descriptions but slightly reduces conciseness.
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 complexity of a 6-parameter music generation tool with no annotations and no output schema, the description provides comprehensive context including purpose, usage guidelines, detailed parameter semantics, behavioral constraints, and return information. The main gap is the lack of output schema details, but the description compensates reasonably well.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing detailed semantics for all 6 parameters. It explains each parameter's purpose, provides examples, character ranges, supported values, defaults, and usage notes (like lyric structure tags and character counting rules). This adds substantial value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Create a music generation task', 'Generate music from prompt and lyrics') and identifies the resource (music). It distinguishes from siblings like text_to_audio or voice_clone by focusing specifically on music generation with AI models, not just audio conversion or voice manipulation.
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 guidelines with the 'COST WARNING' section specifying when to use ('Only use when explicitly requested by the user') and when not to use (avoid unnecessary API calls to Minimax due to costs). It also distinguishes from alternatives by noting it's specifically for music generation, not other audio or media 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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