aihub-mcp

Server Quality Checklist

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Latest release: v0.0.1
Disambiguation5/5
Every tool has a clearly distinct purpose. Generation tools are separated by media type (audio, document, image, video), analysis is separate, task management tools differentiate polling (get_task) from blocking (wait_for_task), and other utilities (embeddings, credits, file upload, model listing) are unique.
Naming Consistency5/5
All tools follow a consistent verb_noun snake_case pattern: analyze_media, ask_model, create_embeddings, etc. No mixed conventions or abbreviations.
Tool Count5/5
14 tools is an appropriate count for a multimodal AI hub. It covers key functionalities without being bloated or sparse.
Completeness5/5
The tool set is comprehensive: media analysis, model querying, embeddings, generation (audio, document, image, video), file upload/download, task management, credits, and model discovery. No obvious gaps for the intended domain.
Average 4/5 across 14 of 14 tools scored. Lowest: 3.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 15 commits in the last 12 weeks
- Last stable release on August 19, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states basic functionality and OpenAI compatibility, without disclosing behavioral traits like rate limits, idempotency, input size constraints, or return format. For a tool generating embeddings, return format (e.g., array of floats) is crucial but omitted.
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 very short (two sentences) and front-loaded with the core purpose. It uses bullet-like clarity but lacks structured sections. Every sentence adds value, but could be more efficient by combining model info and input type into one line.
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 no output schema, the description should explain what the tool returns (e.g., embedding vectors). It does not. Given the tool's complexity (two parameters, straightforward return), the description is incomplete as it omits both output format and behavioral details like error handling or maximum input length.
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 coverage is 100% with basic descriptions, but the description adds concrete model ID examples (text-embedding-3-small, jina-embeddings-v3) and clarifies input as string or array. This provides practical value beyond the schema alone.
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 tool name 'create_embeddings' and description '生成文本向量嵌入' clearly state the action (generate) and resource (text vector embeddings). The description explicitly mentions OpenAI compatibility, which distinguishes it from other generation tools like generate_audio or generate_image. It's specific and non-tautological.
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 over siblings (e.g., ask_model for semantic search, or generate_document for text output). The description does not mention prerequisites, exclusions, or preferred contexts. The agent is left to infer usage from the tool's name alone.
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?
The description discloses polling behavior (every 5s) when wait_seconds>0, and notes that results contain temporary URLs valid for 24 hours. This adds useful behavioral context beyond a simple 'query' statement.
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?
Two concise sentences with essential information front-loaded. No unnecessary words, every sentence adds value.
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 full schema coverage and no output schema, the description adequately explains return format (URLs for media) and validity period. Missing error handling details but sufficient for a status check tool.
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 baseline is 3. The description adds overall behavioral context but does not provide additional parameter-specific semantics beyond what the schema already describes.
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 '查询异步任务' (query async task), indicating the tool retrieves status and results. It is specific but does not differentiate from sibling 'wait_for_task', which may serve a similar purpose.
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 explains behavior when wait_seconds>0 but provides no explicit guidance on when to use this tool versus alternatives like 'wait_for_task' 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the task is time-consuming, the default wait is 60s, short tasks return a URL, and long tasks return a task_id. This is good behavioral context, though it could further detail error handling or failure modes.
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, front-loaded with the main action, then efficient detail. Every sentence earns its place with no wasted words.
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 long-running generation task and no output schema, the description covers the essential workflow: prerequisite (describe_model), default wait, and fallback to get_task. It could mention error handling or output format more explicitly, but overall it is complete enough 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 100%, so parameters are fully documented there. The description adds workflow context (use describe_model for params, wait/retry behavior) but does not provide additional semantic details beyond the schema. 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 clearly states '提交视频生成任务' (submit a video generation task), a specific verb+resource. While it does not explicitly differentiate from sibling tools like generate_audio or generate_image, the tool name and title already do that. The description adds the long-running nature, enhancing clarity.
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 advises using describe_model first to confirm parameters and mentions fallback to get_task on timeout. This provides sequential context but lacks explicit when-to-use versus alternatives or when not to use. Guidance is implied rather than explicit.
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 carries full burden. It explains synchronous/asynchronous behavior and the need to consult describe_model. However, it does not disclose auth requirements, rate limits, or what the returned URL/task_id contain. The inline_image parameter description is irrelevant for audio and may confuse.
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 compact but includes an irrelevant detail about inline_image only applying to generate_image, which is noise for this tool. The structure is mostly front-loaded but could be 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?
Covers the main workflow (submit, wait, fallback) and dependency on describe_model. Lacks details on return format (URL or task_id contents) and potential limitations like credit costs. Without output schema, more detail would improve completeness.
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 coverage is 100%, but the description adds value: it explains the params object structure (e.g., {prompt, aspect_ratio}) and refers to describe_model for details, clarifies wait_seconds=0 behavior, and explains inline_image only applicable to generate_image. This goes beyond the 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 it submits audio tasks (TTS, music, voice recognition, voice cloning) and mentions a dedicated endpoint for Suno. It distinguishes from sibling tools that handle other media types.
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?
Explicit guidance includes using describe_model first, default 60s wait, short tasks returning URL directly, long tasks returning task_id with get_task, and setting wait_seconds=0 for immediate task_id. It does not explicitly state when not to use, but context implies audio-only tasks.
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 provided. Description does not disclose behavioral traits such as file size limits, authentication requirements, rate limits, or what happens with invalid files. It only states the input options and output, leaving significant gaps.
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?
Extremely concise: two sentences that immediately convey purpose, usage context, and input options. Every sentence is meaningful and front-loaded.
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 file upload tool with no required parameters, the description covers key aspects: purpose, input methods, and target usage. Lacks return structure details but still sufficient given low complexity.
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 coverage is 100%. Description adds value by explaining the mutual exclusivity of path, url, base64 and that file_name influences MIME inference, which goes beyond 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?
Description clearly states the tool's verb (upload/转存), resource (local file or remote URL), and output (AihubMax URL). It distinguishes from sibling tools like download_asset by specifying the direction and use in image/video generation.
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?
Description specifies when to use: for scenarios needing image_urls/audio_url in AihubMax. It lists three input methods (path, url, base64) but does not explicitly state when not to use or mention alternatives. Still clear context.
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?
Discloses blocking behavior, MCP progress notifications, timeout returns still-running with latest status, ability to re-call. No annotations provided, so description carries burden well.
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, front-loaded with purpose, then mechanism, then use case. No extraneous 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?
Describes timeout return behavior, but does not explicitly mention success/failure return details. No output schema, so some gaps remain.
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 covers 100% of parameters with descriptions. Description adds overall behavioral context (timeout meaning) but per-parameter detail is already in 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?
Description states it blocks waiting for async task completion/failure/timeout. Clear verb+resource, but no explicit differentiation from sibling tool 'get_task'.
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?
Mentions suitability for long tasks (video), and explains timeout behavior and re-callability. Does not explicitly state when not to use.
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 present, so the description carries full burden. It discloses data source, unit meaning, and unlimited flag, but lacks explicit statements about read-only nature, authentication requirements, or side effects.
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?
Four sentences are concise and front-loaded: main purpose first, then data source, unit clarification, and special case. Every sentence adds necessary context without redundancy.
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 zero parameters and no output schema, the description fully covers what the tool returns, data origin, unit meaning, and the unlimited flag. No additional information is needed for correct invocation.
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?
No parameters exist in the schema, so description has no burden to explain parameters (baseline 4). The description adds value by explaining output semantics (total, used, remaining) and unit convention.
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 an API key's total, used, and remaining credits, with specific data source (New API billing endpoint) and unit convention explanation. Distinguishes itself from unrelated sibling tools.
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 checking credits but does not provide explicit when-to-use or exclusionary context. No alternatives are needed as no sibling credit tools exist.
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 bears the full burden of behavioral disclosure. It explains the output fields 'available', 'price', and 'group_ratios' and their interpretations (e.g., 'available=true' means directly callable). This provides sufficient transparency for a read-only listing tool, though it omits details on pagination 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded paragraph that states the core function first. It is concise with no redundant sentences, but the explanations of 'available', 'price', and 'group_ratios' could be slightly more integrated. Overall, it is well-structured for quick comprehension.
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 absence of an output schema, the description covers the key response fields (available, price, group_ratios) and filtering capabilities. It does not mention pagination or response format details, but for a listing tool with 3 optional parameters and simple logic, it provides adequate context for an agent to use it 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?
Since schema coverage is 100%, the baseline is 3. The description adds minimal extra meaning beyond the schema: it repeats the filtering concept and explains 'available' (which is not a parameter). The keyword param's description in the schema is already clear, and the description does not provide new formatting or constraints.
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 '列出可用生成模型' (list available generation models) and specifies the supported media types (图像/视频/音频/文档). It distinguishes itself from sibling generation tools like generate_image by focusing on discovery rather than creation, and explicitly mentions filtering by media_type or keyword, leaving no ambiguity about the tool's 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 implies usage context by explaining the 'available' field's meaning (directly callable vs. not provisioned), which helps agents decide which model to use for generation tasks. However, it lacks explicit guidance on when to use this tool versus alternatives like analyze_media or get_task, and does not specify prerequisites or exclusions.
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?
Without annotations, the description covers key behaviors: non-streaming, async with 60s default wait, immediate return for short queries, and timeout handling via task_id. It does not mention rate limits or side effects, but is sufficient.
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 paragraph that front-loads the purpose and efficiently covers all essential aspects without redundant or unnecessary information.
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 description covers the main behavior and parameter interactions, but lacks detail on the return value structure (e.g., format of direct text response) and error cases. Given no output schema, this is a notable gap.
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 meaning beyond the schema by explaining the mutual exclusivity of prompt and messages, the role of system with prompt mode, and the behavior of wait_seconds (default 60, 0 returns task_id). Schema coverage is 71%, so description compensates well.
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 as calling an LLM for dialogue, positioned as seeking a second opinion or trial, and distinguishes it from the main chat channel and sibling tools like media/audio generation.
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 explicitly states this tool is for second opinions or trial, not the main chat channel, and mentions async polling with fallback to sibling tools get_task/wait_for_task. It provides clear context although not exhaustive.
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 must disclose behavior. It covers async execution and timeout behavior, but includes the 'inline_image' parameter with a note that it only applies to generate_image, which is confusing for this document tool. Does not mention failure handling or credential needs.
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?
Two concise sentences that front-load the purpose and immediately provide actionable guidance. No wasted words.
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 no output schema, the description explains return values (URL or task_id) and workflow. However, it does not address errors or credits, and the inline_image parameter seems out of place. Still, the core async behavior is well-covered.
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% with descriptions for all 4 parameters. The description adds context about using describe_model for params and explains wait_seconds, but the inline_image parameter's note is misleading. Overall, marginal value added 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 the tool submits document processing tasks (PDF to md/tex/docx) with a specific verb '提交' and resource '文档处理任务'. It distinguishes from sibling tools like generate_audio, generate_image, etc., by focusing on document conversion.
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?
Explicit guidance: use describe_model first to confirm parameters, default wait 60s, short tasks return URL, long tasks return task_id for get_task. It also explains wait_seconds behavior and mentions get_task as a follow-up.
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, the description bears full burden. It discloses core behavior: submitting a task, waiting up to 60s, short tasks return URL, long tasks return task_id. It also explains the inline_image parameter behavior and the effect of wait_seconds. Minor omission: no mention of error handling or cost implications, but sufficient.
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: three sentences, front-loaded with purpose, then prerequisite, then behavior. No redundancy or unnecessary information. Every sentence 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 no output schema and 4 parameters with nested objects, the description explains async behavior, prerequisites, and follow-up (get_task). It covers the return values (URL or task_id) adequately. Could mention URL expiry, but not essential. Complete enough for common use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description reinforces the guidance to use describe_model for params format, but does not add significant new meaning beyond the schema. The schema already describes each parameter adequately.
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 '提交图像生成/编辑任务' (submit image generation/editing task), specifying the verb (submit/generate/edit) and resource (image). It distinguishes from sibling tools like generate_audio and generate_video by focusing on 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 description advises to first use describe_model to confirm model parameters, and explains the default wait behavior with short vs long tasks, explicitly directing to use get_task for long tasks. It lacks explicit when-not-to-use but provides strong contextual 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, the description carries the full burden. It discloses asynchronous submission, default 60s wait, direct return vs task_id for long tasks, media type auto-detection, and model routing. However, it does not mention rate limits or detailed error conditions beyond invalid model.
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 paragraph that front-loads the core purpose and then provides key details. It is concise and logically ordered, though could benefit from slight structuring for readability.
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 (9 params, no output schema), the description covers purpose, input types, async behavior, model source, and error handling. It compensates for missing output schema by describing return types. Minor gaps like rate limits and full error scenarios are absent.
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 coverage is 78%, so baseline is 3. The description adds value by explaining the async logic (wait_seconds, task_id return) and the model registry distinction, which goes beyond what the schema provides. It does not repeat parameter constraints but contextualizes their use.
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: analyzing media (image/video/audio) using multimodal LLMs to output text, and explicitly distinguishes it from the host model's limitations. It differentiates from sibling tools like ask_model (text-only) and generate_* tools.
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 explains when to use the tool (for media analysis), describes the asynchronous behavior and timeout handling, and notes that models come from a separate registry from generation tools. While it doesn't explicitly mention when not to use, it provides clear contextual 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?
No annotations are provided, so the description carries full burden. It describes the return values (endpoint, parameters, example) and suggests safe usage (avoiding 422). It does not mention destructive actions, which is appropriate for a read-only query 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 two sentences, front-loaded with core functionality and usage benefit. Every word is informative; no redundant or vague phrasing.
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 with one parameter and no output schema, the description fully explains what is returned (endpoint, parameters with types/enums/defaults/descriptions, example request) and the use case (pre-generation parameter confirmation). No 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?
Schema description coverage is 100% for the single parameter 'model', with an example ID provided. The tool description adds context about the parameter's purpose (model ID) but does not significantly expand beyond the schema. Baseline 3 is appropriate.
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 returns the submission endpoint, required/optional parameters with details (types, enums, defaults, Chinese descriptions), and an example request. It distinguishes from siblings like list_models by focusing on parameter details for a specific model, not just listing models.
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 explicitly advises to use it before generation to confirm parameters and avoid 422 errors. It implies the context of use (before generating) but does not explicitly state when not to use or mention alternatives.
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 are provided, so the description carries the full burden. It discloses that the tool downloads and saves files locally, that URLs expire, and the parameter behaviors. It does not mention permissions or side effects beyond saving, but for a download tool this is sufficient.
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, consisting of two sentences that front-load the main purpose. Every sentence adds essential information without redundancy.
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 no output schema, the description does not explain return values (e.g., saved file path or success status). While the tool's behavior is mostly clear, the lack of return value information is a gap for an otherwise complete description.
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 coverage is 100% with parameter descriptions. The description adds context beyond the schema: it explains the relationship between task_id and url (mutually exclusive), the purpose of saving large files, and the URL expiry. This adds meaningful value.
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: downloading task assets or arbitrary URLs to local disk. It specifies the two modes (by task_id or direct URL) and highlights that it's for explicit persistence of large files like videos.
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 explains when to use each parameter (task_id vs url) and notes the 24-hour URL expiry. While it doesn't explicitly mention when not to use the tool, the sibling tools are distinct and do not offer download functionality, so the guidance is adequate.
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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