gemini-media-mcp

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Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool targets a distinct media type and action: image generation, editing, composition, animation; video generation, extension, status check, download; audio and music generation; plus utility tools for config and model listing. No overlaps in purpose.
Naming Consistency4/5
Most tool names follow a clear verb_noun pattern (e.g., generate_image, download_video). The only exception is video_status, which reverses the pattern to noun_verb, but it's still easily understood and doesn't cause confusion.
Tool Count5/5
12 tools is well-scoped for a media generation server covering images, video, audio, and music. Each tool serves a clear purpose without redundancy, and the async video workflow is handled with dedicated tools.
Completeness5/5
The tool surface covers key operations for each media type: create, edit, compose (images), generate, extend, monitor, download (video), and generate (audio, music). The async video workflow is fully supported, and utility tools provide necessary context. No obvious gaps.
Average 3.8/5 across 12 of 12 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on April 7, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden for behavioral disclosure. It does not mention whether generation is synchronous, costs, safety filters, or model behavior differences between 'nb2' and 'pro'.
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?
Single sentence, 12 words, front-loaded with the primary action. No redundant or extraneous 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?
Adequate given schema covers parameters and output schema likely documents returns. But missing behavioral context like typical latency, cost, or model selection guidance.
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?
Input schema has 100% coverage with descriptions for each parameter. Description adds no additional meaning beyond schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool generates an image from a text prompt using Gemini models. It specifies the resource (image) and verb (generate), but does not explicitly differentiate from sibling tools like edit_image or animate_image.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives. The description does not mention prerequisites, limitations, or mention use cases that favor other tools (e.g., editing, animation).
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 full burden for behavioral disclosure. It only reveals the async operation. Missing information includes authentication requirements, default behavior, rate limits, cost implications, and whether the tool is destructive or creates a temporary resource. This is insufficient for a generation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences with no superfluous words. The first sentence states the core purpose, and the second provides critical workflow guidance. Every word earns its place.
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 complexity (5 parameters, async operation) and the presence of an output schema, the description is adequate but not complete. It covers the async flow but omits information about immediate return values (likely a job ID), error handling, and expected timeouts. The output schema may fill some gaps, but the description could offer a brief hint.
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 has 100% description coverage with detailed explanations for each parameter. The tool description does not add any additional semantics beyond the schema; it merely summarizes the overall task. Baseline score of 3 is appropriate as the schema already provides the necessary information.
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 a video from a text prompt using Gemini models. The verb 'generate' and resource 'video' are specific. While it does not explicitly differentiate from sibling tools like animate_image or generate_image, it does reference related tools for the async workflow.
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 explains the async nature and directs the user to video_status for polling and download_video for retrieval, which provides good usage context. However, it does not mention when not to use this tool (e.g., for image animation) or list alternatives, relying on sibling context provided externally.
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 exist, so the description must cover behavioral traits. It only says 'edit' without disclosing side effects like file overwriting, return behavior, or permission requirements. The mutation nature is implied but not detailed, 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?
A single sentence (18 words) that front-loads the verb and resource, with zero wasted words. Every part earns its place, achieving maximum conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given an output schema exists (so return values need not be explained) and only 3 simple parameters, the description is largely sufficient. It omits details like model default but schema covers it. For a straightforward tool, it is 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?
Schema description coverage is 100%; baseline is 3. The description adds 'Provide the path to the source image and a description of the desired changes', which modestly rephrases prompt and imagePath but adds no new semantics for the model parameter. It does not exceed baseline 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 'Edit an existing image using a text prompt', specifying the verb (edit), resource (existing image), and method (text prompt). It distinguishes from sibling tools like generate_image (creates new) and animate_image (animates), making the purpose unambiguous.
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 implies usage when an existing image needs modification but provides no explicit guidance on when to use this tool versus alternatives like generate_image, animate_image, or compose_images. No when-not or exclusion criteria are mentioned.
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 disclose behavioral traits. It fails to mention important details such as cost, rate limits, support for long texts, synchronous vs async processing, or output format/quality. The description only covers basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that conveys the core functionality without unnecessary words. Every phrase earns its place.
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?
While the description covers the primary purpose and key parameters, it lacks completeness for a tool with no annotations. It omits behavioral context, error handling, and prerequisites. However, the presence of an output schema partially compensates for omitted return value details.
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?
With 100% schema description coverage, the baseline is 3. The description adds minimal semantic value by mentioning 'voice selection and language configuration,' but does not provide examples, constraints, or interaction details beyond what the schema already offers.
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 generates speech audio from text, specifies the underlying service (Google's Gemini TTS), and mentions configurable options (voice, language). This distinguishes it clearly from sibling tools like generate_music or generate_video.
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 speech generation but provides no explicit guidance on when to use this tool over alternatives (e.g., other audio generation tools) or any exclusions or prerequisites. It lacks 'when not to use' or references to sibling tools.
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 fully disclose behavior. It only says 'compose' with references and prompt, but fails to explain side effects (e.g., file creation), permission needs, or whether the process is synchronous. This is insufficient for a transformation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 20 words, front-loading the key action. Every word is functional, with no redundancy or filler.
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 complexity (4 parameters, composition task) and the presence of an output schema, the description should explain the composition process, constraints, or default behavior. It does not, leaving gaps about how images are merged, resolution limits, or effect of the prompt.
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?
With 100% schema coverage, baseline is 3. The description adds minimal value beyond the schema: it repeats that reference images are 1-3 but does not elaborate on model or aspectRatio. No extra context or constraints are provided.
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 function: composing a new image using reference images and a text prompt. The verb 'compose' and resource 'image' are specific, and it distinguishes from sibling tools like generate_image (no reference images) and edit_image (modifies existing).
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 the tool should be used when 1-3 reference images are available and textual guidance is wanted. However, it lacks explicit when-not-to-use guidance or comparison to alternatives like generate_image or animate_image.
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; the description only describes what the tool generates (music) and supported prompt features. It does not disclose behavioral traits such as whether the operation is destructive, required authentication, rate limits, or output format beyond the existence of an output schema.
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 precise sentences with no extraneous words. The core action and supported features are front-loaded, making it efficient for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists, the description need not explain return values. It covers the input capabilities (genres, structure tags, lyrics) and model choices. Could mention prerequisites or limitations, but is largely adequate 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 covers 100% of parameters. The tool description repeats the prompt details found in the schema and adds no new meaning. The model parameter is already fully described in the schema. Baseline 3 due to 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?
Specifically states 'Generate music from a text prompt using Google's Lyria models,' clearly identifying the verb, resource, and tool. Distinguishes from sibling tools like generate_image or generate_audio by focusing on music 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?
Clear that this tool is for music generation from text, but does not explicitly state when not to use it or mention alternatives like generate_audio. Context from sibling tools makes the distinction obvious, so the guidance is implicit 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 covers return states but not side effects, idempotency, rate limits, or auth needs. Minimal but adequate for a basic status check.
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 sentences, front-loaded with purpose, no redundant words. 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?
Simple tool with one parameter and output schema present. Description sufficiently explains purpose and return semantics for the context.
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% and already describes the parameter fully. The tool description does not add extra meaning beyond what the schema provides.
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 verb ('check') and resource ('status of an async video generation operation') and lists possible states. It is specific and distinguishes from sibling tools like generate_video.
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 implies usage after an async operation but does not explicitly state when to avoid it or compare to siblings like download_video. No exclusions or alternatives mentioned.
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 the full burden. It discloses the async behavior and polling requirement, but lacks details on error handling, rate limits, or what happens on invalid inputs. This 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences cover purpose, required inputs, async nature, and next steps. No redundancy, front-loaded with the core action. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown), the description provides enough context for correct usage: calls to video_status and download_video are mentioned. However, it does not cover error handling or output format specifics.
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 parameters are well-described in the schema. The description adds only minor context (e.g., 'prompt guiding the animation'), not significantly improving over 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's function: 'Animate a still image into a video clip.' It specifies the required inputs (image path and prompt) and distinguishes itself from sibling tools like video_status and download_video by noting it is the async start.
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?
It explicitly explains the async nature and guides the agent to use video_status and download_video for results. However, it does not provide alternative scenarios or when not to use this tool versus other similar tools.
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 implies a read-only operation, but with no annotations, it does not disclose additional behavioral traits like authentication needs, rate limits, or side effects. Basic transparency is present, but gaps remain.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no wasted words. It efficiently conveys the tool's purpose.
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 main aspects of the tool given no parameters and an output schema. However, it only lists examples ('including') rather than a comprehensive overview, leaving minor ambiguity.
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?
There are no parameters, so schema coverage is 100% vacuously. The description adds value by specifying what the output includes (active backend, output directory), which aids understanding beyond the empty 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 shows current server configuration, mentioning specific elements like active backend and output directory. This distinguishes it from sibling tools that generate or manipulate media.
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 is clear, but no explicit guidance is given for when not to use or alternatives. Since siblings are all different functions, the usage context is implied, but formal guidelines are missing.
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 adequately conveys that this is a download (non-destructive) operation and requires a completed status. Lacks details on auth or rate limits but is sufficient for a simple 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?
Two sentences, front-loaded with purpose, followed by a precondition. No wasted words; 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 tool with one parameter and an output schema, the description covers the core usage and precondition. No missing information given 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?
The single parameter 'operationId' is fully described in the input schema, which already explains its origin (from generate_video, animate_image, or extend_video). The description adds no further meaning, so baseline 3 applies given 100% 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?
Clearly states the action (download) and resource (completed video generation). Distinguishes from siblings like generate_video and video_status by specifying it is for downloading completed videos only.
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?
Explicitly instructs to call only after video_status reports completion, providing a clear precondition. Though it does not name alternatives, the instruction is specific enough to prevent misuse.
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, and the description adds minimal behavioral context beyond listing information. It does not disclose auth requirements, rate limits, or that it is read-only (though implied). 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with 'List the models', includes specific details without unnecessary words. Highly concise and well-structured.
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?
With zero parameters and an output schema, the description sufficiently describes the tool's purpose and return content. Complete for a simple list tool.
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, so baseline 4 applies. The description is not required to add parameter meaning.
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?
Clearly states the verb 'List' and resource 'models supported by this server', and specifies included details (tiers, capabilities, supported resolutions, pricing guidance), distinguishing it from sibling generation 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?
Does not explicitly state when to use vs alternatives, but the self-explanatory name and description imply it is for model discovery before using generation tools. No exclusions mentioned.
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 full burden. It discloses the async operation and the need for a matching model. It does not detail side effects or error states, but the workflow is transparently 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?
Two concise sentences immediately convey the tool's purpose, prerequisite, and async workflow without any redundant or extraneous 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?
The description covers the core workflow (async, poll, download) and important constraints (model compatibility). Since an output schema exists, the omission of return value details is acceptable. Minor gaps in error handling or status polling frequency are not critical.
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 valuable semantics: that the model must match the original and that Lite does not support extension. This goes beyond the schema's basic 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 precisely states it extends a previously generated video using a continuation prompt and requires an operation ID. It clearly distinguishes from siblings like generate_video or animate_image by specifying the prerequisite operation ID.
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?
It explicitly mentions the requirement of the operation ID and the async nature, directing to video_status and download_video for polling and retrieval. It does not explicitly exclude other use cases or provide when-not-to-use scenarios, but the context is clear enough.
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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