mcp-minimax

Server Quality Checklist

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Latest release: v0.1.2
Disambiguation5/5
Each tool targets a distinct operation: discovery (list_models, list_actions), task management (list_tasks, get_task, get_tasks_batch, delete_task), and generation (text/images/audio/full schema). Even the similar generation tools are clearly separated by input type and explicit mode constraints.
Naming Consistency5/5
All tools follow a strict snake_case verb_noun pattern with a consistent 'minimax_' prefix. The pattern is uniform: list_*, get_*, delete_*, generate_video_from_*. Minor differences like get_tasks_batch still match the convention.
Tool Count5/5
Ten tools is well-scoped for a video generation server. Each tool covers a necessary part of the workflow—discovery, generation (four input modes), tracking, and deletion—without redundant or superfluous entries.
Completeness5/5
The tool surface provides complete lifecycle coverage: describe capabilities, generate videos from all supported input types, query single/batch tasks, and delete tasks. No obvious dead ends or missing critical operations for the stated domain.
Average 3.6/5 across 10 of 10 tools scored. Lowest: 1.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 19 commits in the last 12 weeks
- Last stable release on August 23, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description omits key behavioral aspects such as async execution (returns a task_id), the need for polling via minimax_get_task, or callback handling. This leaves critical operational details undisclosed.
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 sentence and is concise, but it is too vague to be effective. It is not verbose, but the lack of content prevents it from being well-structured with useful information.
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 tool is complex with 7 parameters and requires understanding of the content schema's composition. The description provides almost no context, leaving the agent without sufficient information to properly construct a request or handle the async workflow.
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 high parameter description coverage (100%), so the baseline is 3. The tool description adds no extra meaning beyond the schema, but since the schema already explains parameters, this is adequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Generate a video from the full MiniMax content schema,' which indicates the tool generates videos but does not specify what content types are supported or how it differs from sibling tools like minimax_generate_video_from_text or from_images. It is not specific enough to guide selection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this general tool versus the specialized generation tools. Given the many sibling tools, the description fails to provide any usage criteria, making it difficult for an agent to choose correctly.
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 says 'Describe', which implies no side effects, but does not explicitly state whether it is read-only, what kind of data it returns, or any error conditions. The description is too thin for a tool with zero 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler or repetitive content. It efficiently states the action and the target. There is no wasted verbosity.
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 the tool has no parameters and an output schema exists, the description is minimally acceptable. However, the mismatch between the tool name ('list_models') and the description ('Describe the MiniMax H3 model') leaves uncertainty about whether the tool lists all models or provides a single model's details. The output schema likely clarifies this, but the description alone is incomplete.
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 baseline is 4. The description does not need to explain parameter syntax, and it correctly implies that no inputs are required. No further parameter semantics are necessary.
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 'Describe' and a specific resource 'MiniMax H3 model', making the tool's primary purpose evident. However, the tool name 'list_models' suggests listing multiple models, while the description narrows to a single model, creating slight ambiguity about the actual scope.
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?
There is no guidance on when to use this tool versus alternatives. It does not state any context, prerequisites, or exclusions. Sibling tools are mentioned but not referenced, so the agent gets no help in choosing between them.
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 must carry the full burden of behavioral disclosure. While 'Delete' implies mutation, the description does not specify whether deletion is permanent, whether it requires any confirmation or special permissions, or whether it can fail under certain conditions. It also doesn't mention what happens to the task data or resources.
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, compact sentence that conveys the essential purpose without any fluff or redundancy. It is appropriately front-loaded and efficient.
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 relative simplicity (one parameter, no nested objects, clear action) and the presence of an output schema, the description is adequate but not rich. It does not fully compensate for the lack of annotations, particularly around behavioral risks like permanence. However, the output schema may provide some post-deletion context that is not visible here.
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% for the single parameter (task_id) with a clear description 'The task ID to delete.' The tool description adds no additional meaning beyond that, but the schema already sufficiently documents the parameter.
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 ('Delete') and resource ('a MiniMax video generation task'). It is specific and distinguishes from siblings (e.g., minimax_get_task for retrieval, minimax_list_tasks for listing). However, it doesn't elaborate on the scope or effect of deletion beyond the basic action.
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 deleting tasks, but does not provide explicit guidance on when to use it vs. alternatives. It doesn't mention context like how to obtain the task_id or whether deletion is permanent, nor does it exclude any scenarios. Sibling tools are not referenced.
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 only states that a video is generated, offering no information about asynchronous execution, task creation, return values, rate limits, or the need to poll for completion. This is minimal and leaves important operational behavior undisclosed.
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 that immediately states the core purpose and front-loads the key information. There is no filler or redundant repetition of the tool name; every word contributes to understanding what the tool does.
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?
The tool has a rich schema and output schema, so parameter details are already covered. However, the description does not mention the async/task-polling workflow or distinguish itself from the generic minimax_generate_video sibling. It is minimally viable but lacks contextual guidance for a multi-step video generation workflow.
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 all 7 parameters with descriptions, including prompt, ratio, duration, resolution, async, model, and callback_url. Baseline is 3 for high schema coverage. The description itself adds no additional meaning beyond what the schema already documents, so no higher score is warranted.
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 MiniMax H3 video from a text prompt,' naming the specific verb (generate), the resource (MiniMax H3 video), and the input type (text prompt). This distinguishes it from sibling tools like minimax_generate_video_from_images and minimax_generate_video_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 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 minimax_generate_video_from_images or minimax_generate_video. It does not mention async behavior, nor that results should be polled via minimax_get_task. Usage is only implied by the tool name rather than explicitly stated.
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?
The description mentions 'per MiniMax H3 v2 API contract', hinting at API-specific behavior, but does not disclose potential side effects (e.g., cost, processing time) or return behavior. Since annotations are absent, it carries the full burden but falls short of full 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 concise, using three short sentences to convey the core functionality and mode constraints. Every sentence adds value, with no redundant or verbose content.
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?
The description covers the essential aspects: what the tool does (generate video from images), the two input types, and the mutual exclusivity. It is sufficient for basic usage, though it could mention typical use cases or limitations (e.g., only supports certain models).
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 does not add new information beyond the schema, which already covers parameters like image_urls, prompt, ratio, duration, etc. However, by clarifying the mode distinction, it indirectly helps interpret the image_urls parameter. The schema coverage is 100%, so the description only slightly enhances understanding.
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 generates videos from images, distinguishing it from text/audio generation siblings by mentioning 'first-frame image' and 'reference images'. It specifies the two modes, making the purpose concrete.
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?
It explains the two modes (first_frame vs reference_image) and notes they are mutually exclusive, which guides usage. However, it does not explicitly compare with sibling tools like generate_video_from_text or generate_video_from_audio, so when to choose this tool over others is implied rather than stated.
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 burden of disclosing behavior. It states the tool lists tasks and allows optional timestamp filtering, but does not disclose whether it returns all tasks in a paginated manner, the default ordering (e.g., chronological vs reverse chronological), or any rate-limiting or authentication requirements. Given the lack of annotations, a more detailed behavioral description is needed.
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 that is clear and to the point. It effectively communicates the tool's purpose and the main feature (filtering by timestamp) without unnecessary detail. Every word 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?
The tool has a simple list operation with well-defined parameters and an output schema, so the description is mostly sufficient. However, it does not explicitly mention pagination behavior (though limit/offset are implied), nor does it clarify the ordering or how to interpret the results in relation to the output schema. The presence of an output schema and 100% parameter coverage mitigate the need for extensive description, but a bit more detail on expected behavior 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 coverage is 100%, so all four parameters are described in the schema. The description adds the mention of timestamp filtering but does not provide additional context beyond the schema definitions. Since the schema already covers the parameters, the description's contribution is minimal, earning a baseline 3.
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 lists video generation tasks and indicates optional filtering by timestamp. This distinguishes it from sibling tools like minimax_list_models (which lists models) and minimax_get_task (which likely retrieves a single task). However, it doesn't explicitly distinguish from minimax_get_tasks_batch or mention the output format.
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 this is for listing tasks rather than retrieving a specific one, but it doesn't explicitly state when to use this tool vs minimax_get_tasks_batch or minimax_get_task. There's no guidance on when filtering is appropriate or when to use each sibling tool. The context signals show sibling tools with similar purposes (get_tasks_batch), so more explicit usage guidance would be beneficial.
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 transparency burden. It reveals the reference-mode behavior and mentions a contract, but omits side effects, permissions, rate limits, or async processing details. The note about exclusive reference mode adds some transparency 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, concise, and without fluff. It delivers the essential purpose and a key behavioral nuance efficiently.
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 that an output schema exists, return values need not be explained. The description explains the audio-guided mode and is clear about inputs, but it does not cover edge cases like missing audio or validation rules. Overall, it is suitably complete for the tool's complexity.
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% with each parameter already described. The tool description adds no parameter-specific semantics beyond reiterating the reference-mode behavior, so it meets the baseline for full schema coverage.
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 generates a video guided by audio and reference images, and explicitly notes the exclusive reference mode for audio-guided generation. This distinguishes it from siblings like generate_video_from_text and generate_video_from_images.
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 purpose implies usage when audio and reference images are available, but it does not explicitly contrast with alternatives. The reference-mode detail provides context, but a direct 'use this when you have audio' guideline is absent.
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?
The verb 'List' implies a read-only, non-destructive operation, which provides basic transparency. However, with no annotations and no further disclosure about return format, pagination, or limitations, the description carries only minimal behavioral information.
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 wasted words. It states the action and resource directly, making it highly concise and efficient.
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 the tool's simplicity (no parameters, no nested objects) and the presence of an output schema, the description is sufficient. It clearly communicates the tool's purpose without needing to explain return values, which are covered by the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so schema coverage is effectively 100%. Per the rubric, a zero-parameter tool gets a baseline of 4, and the description adds no parameter semantics because there are none to describe.
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 the specific verb 'List' and identifies the resource as 'MiniMax H3 generation and task tools,' clearly differentiating it from sibling tools like minimax_list_models or minimax_generate_video_from_text. It is unambiguous and not a tautology.
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 its usage for discovering available generation and task tools, but provides no explicit guidance on when to use it versus alternatives. There is no mention of prerequisites or exclusions, though the context of a listing tool makes the usage somewhat self-evident.
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 discloses that the operation is a query and mentions the return type generally, but does not discuss behavior for invalid task IDs, partial failures, or rate limits. It adds some context about efficiency but lacks richer behavioral detail.
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 well-structured with a clear opening statement, an efficiency note, a bulleted usage list, and a returns section. Every sentence serves a purpose, making it concise and front-loaded without unnecessary fluff.
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 simple single-parameter batch query with an output schema, the description covers purpose, usage, and efficiency context. It lacks explicit error-handling behavior, but the low complexity and schema-provided batch size guidance make it nearly 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 input schema provides 100% coverage for the single parameter task_ids, including a description of the ID list and the maximum batch size of 50. The tool description does not add parameter semantics beyond what the schema already documents, so the baseline score of 3 applies.
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 queries multiple video generation tasks at once, using a specific verb and resource. It distinguishes itself from the sibling minimax_get_task by explicitly noting it is more efficient than calling the singular version multiple times.
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 includes a 'Use this when' list with concrete scenarios (multiple pending generations, checking several videos, tracking batches) and explicitly names the alternative minimax_get_task, making the tool selection guidance clear 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?
No annotations, so the description bears the transparency load. It explains the read-only nature implicitly and describes possible states, but does not explicitly state it is side-effect-free. Still, the behavior is well described.
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?
Concise and well-organized with bullet-like lists for use cases and states. No redundant information, every sentence adds value.
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?
For a simple query tool, the description covers purpose, usage, states, and return content. It is sufficient for an agent to understand when and how to call it.
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 single parameter task_id is fully explained both in the schema and in the description, including its origin from generation requests. Coverage is 100%.
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: 'Query the status and result of a video generation task.' It is specific and distinguishes from sibling tools like generate, list, and delete.
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?
Provides explicit use cases ('check if a generation is complete', 'retrieve the resulting video URLs'), and lists task states to inform polling behavior. No ambiguity about when to use.
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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