MiniMax MCP Server

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by MiniMax-AI

Overview Schema Related Servers Score Discussions

Python

Hybrid

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: video generation, voice listing, audio playback, text-to-audio, text-to-image, and voice cloning. No overlapping functionality.
Naming Consistency4/5
Most tools follow verb_noun pattern (generate_video, list_voices, play_audio, voice_clone). Two tools use a conversion-style naming (text_to_audio, text_to_image) which is consistent with each other but slightly deviates from the main pattern.
Tool Count5/5
6 tools is well-scoped for a multimedia generation server, covering video, audio, image, and voice features without being overwhelming or too sparse.
Completeness3/5
The tool set covers core creation operations (video, audio, image generation, voice cloning) but lacks management tools like listing, updating, or deleting generated content, which are notable gaps.
Average 3.9/5 across 6 of 6 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- 18 of 39 community issues answered or closed in the last 6 months
- 4 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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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?
No annotations are provided, so the description must fully disclose behavior. It covers supported formats and lack of video support, but does not explain how URLs are handled, error scenarios (e.g., file not found), permissions, or return value details beyond 'Text content with the path'.
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 concise with a single informative sentence followed by structured Args and Returns sections. No redundant information, though it could be slightly more organized.
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 output schema, the description is too brief. It lacks details on prerequisites, error handling, behavior with URLs vs local files, and differentiation from sibling tools like text_to_audio or voice_clone.
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 coverage is 0%, so the description partially compensates by listing parameters and stating that is_url is optional with default false. It adds context about supported formats (WAV/MP3) for input_file_path, but does not describe acceptable URL or path formats.
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 explicitly states 'Play an audio file' and specifies supported formats (WAV, MP3). It also notes 'Not supports video', distinguishing it from sibling tools like generate_video. The verb and resource are clear.
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 for playing audio files but does not explicitly guide on when to use this tool versus alternatives like text_to_audio (for generation) or generate_video (for video). The 'Not supports video' hint is indirect.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must fully disclose behavior. It mentions cost and charging but fails to clarify the role of 'voice_id' (input or output?) and does not explain side effects, prerequisites, or error handling. The ambiguous description of voice_id undermines transparency.
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 somewhat lengthy due to a docstring-style parameter list that partly duplicates the schema. The cost warning is helpful but adds extra length. The core purpose is front-loaded, but the structure could be tighter.
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?
With 5 parameters and no output schema, the description should cover all aspects. It explains return format partly but misses output_directory, prerequisites for audio files, error handling, and the exact meaning of voice_id. This leaves significant gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 compensates by explaining file, text, and is_url. However, voice_id is poorly described as 'the id of the voice to use' without clarifying if it's source or target, and output_directory is omitted entirely.
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 explicitly states 'Clone a voice using provided audio files', clearly identifying the verb (clone) and resource (voice). It distinguishes from siblings like text_to_audio and text_to_image by focusing on voice cloning from 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 cost warning advises to use only when explicitly requested, giving clear when-to-use guidance. However, it does not explicitly mention alternative tools like text_to_audio for simpler cases, leaving some ambiguity.
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?
No annotations are provided, so the description carries the full burden. It states the tool returns text content with the list of voices and mentions the api_host constraint. Basic behavioral info is present, but details on potential side effects, rate limits, or error handling are missing.
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 concise, front-loads the main purpose, and uses a clear Args/Returns structure. Every sentence adds value, though it could be slightly 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 simplicity (one optional parameter, no nested objects, no output schema), the description adequately covers the purpose, condition, parameter, and return type. It lacks notes on pagination or error cases but is sufficient for basic 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?
The schema has no description for the voice_type parameter (0% coverage), but the description explicitly lists possible values (all, system, voice_cloning) and the default. This adds significant meaning 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 'List all voices available', providing a specific verb and resource. The unique constraint about api_host adds clarity, but it does not explicitly differentiate from sibling tools like text_to_audio or voice_clone.
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 includes an important usage condition (api_host must be https://api.minimax.chat) and details the optional voice_type parameter. However, it does not offer guidance on when to use this tool versus alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry full behavioral disclosure. It mentions API call to Minimax and cost, but lacks details on side effects, authorization, rate limits, or error handling. Adequate but not comprehensive.
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 a clear purpose, cost warning, and parameter details. It is somewhat verbose but remains efficient, with front-loaded key 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 complexity (6 parameters, no output schema, no annotations), the description covers all parameters, explains return value, and provides cost context. Missing error handling and edge cases, but reasonably complete for the task.
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 lists all 6 parameters with explanations, including defaults and allowed values (e.g., aspect_ratio options). Since schema description coverage is 0%, this provides essential meaning beyond the schema, though some details like prompt_optimizer effect are minimal.
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 'Generate a image from a prompt', using a specific verb and resource. It distinguishes from sibling tools like generate_video and text_to_audio, leaving no ambiguity about its purpose.
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 includes a cost warning advising to only use when explicitly requested, providing clear context for usage. However, it does not explicitly state when not to use or compare to alternatives, missing full exclusion guidance.
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?
No annotations are provided, so the description must disclose all behavioral traits. It mentions the output file creation and return value, but lacks details on overwrite behavior, error handling, or auth requirements. The cost warning adds some transparency, but overall the description is adequate but not comprehensive.
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, starting with a clear summary, then a cost warning, then a detailed parameter list. It is front-loaded and organized, though slightly verbose in parameter explanations. Still, it earns its place.
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, no output schema), the description covers purpose, cost, parameter meanings, and return type. It is fairly complete, though it could mention file naming conventions or directory creation behavior.
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's Args section provides extensive meaning for all 13 parameters, including ranges, examples, and defaults (e.g., 'speed: 0.5 to 2.0, default 1.0'). This fully compensates for the missing schema descriptions.
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 'Convert text to audio with a given voice and save the output audio file to a given directory,' using a specific verb ('convert') and resource ('text to audio'). It distinguishes from sibling tools like text_to_image and generate_video, as it focuses solely on audio generation from 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 includes a cost warning advising to only use when explicitly requested, providing explicit usage context. However, it does not differentiate from sibling tools like voice_clone or list_voices, which could be relevant when selecting a voice or cloning.
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?
No annotations provided, so description carries full burden. It discloses API call cost and model-specific behavior (camera instructions), but lacks info on rate limits, auth, or what gets destroyed. Adequate but not comprehensive.
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?
Well-structured with cost warning upfront and parameter details indented. Slightly lengthy but every part is useful; could be more concise but not overly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description explains return value (path). All 4 parameters are described, and behavior for different models is covered. Complete for this tool's complexity.
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 coverage, description fully explains all parameters: model values meaning, prompt camera instructions, first_frame_image for I2V, and output_directory. Adds substantial value beyond 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 'Generate a video from a prompt' with a specific verb and resource. It distinguishes from sibling tools (list_voices, play_audio, etc.) which handle audio or images, making video generation unique.
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?
Includes a cost warning explicitly stating to use only when requested, plus details on when to choose Director vs I2V models. Provides clear context for appropriate usage.
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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