Media-infrastructure

Server Quality Checklist

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Latest release: v1.12.6
Disambiguation3/5
Most tools have distinct purposes, but there is notable overlap between get_operation and get_task, which both retrieve task/operation statuses, potentially causing confusion. Additionally, the three estimate_* tools are clearly differentiated by media type, but their naming and purpose similarity could lead to misselection if not carefully read.
Naming Consistency4/5
The naming is mostly consistent with a verb_noun pattern (e.g., generate_image, check_balance, list_tasks), but there are minor deviations like ai_edit (noun_verb) and background_remove (noun_verb). These inconsistencies are few and do not severely impact readability, but they break the overall pattern.
Tool Count4/5
With 16 tools, the count is reasonable for a media infrastructure server covering image, video, and music generation and editing, plus account management. It is slightly on the higher side but well within a manageable scope, as each tool serves a specific function in the domain.
Completeness4/5
The tool set provides good coverage for media generation and editing, including CRUD-like operations (generate, edit, upscale, list, get status) and cost estimation. However, there are minor gaps, such as no explicit tool for deleting tasks or managing media assets beyond generation and editing, which agents might need to work around.
Average 3.6/5 across 16 of 16 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
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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

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 mentions a specific cost ('$0.68 (Suno V5)') which hints at pricing, but doesn't disclose behavioral traits like whether this is a read-only operation, if it requires authentication, rate limits, or what the output format looks like. For a cost estimation tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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—just one sentence with no wasted words. It front-loads the core purpose and includes a specific cost detail that's directly relevant. Every part of the description earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It mentions a base cost but doesn't explain how the estimation works, what factors influence cost beyond duration and format, or what the return value looks like. For a cost estimation tool with two parameters, this leaves too many unanswered questions about functionality.
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 the schema fully documents both parameters (duration and audio_format). The description adds marginal value by noting 'WAV adds +$0.10' for the audio_format parameter, but doesn't provide additional context beyond what's in the schema. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Estimate the cost of music generation.' It specifies the resource (music generation) and verb (estimate cost). However, it doesn't explicitly differentiate from sibling tools like 'estimate_cost' or 'estimate_video_cost', which reduces clarity about when to choose this specific music-focused estimator.
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. It doesn't mention sibling tools like 'estimate_cost' (general) or 'estimate_video_cost' (video-specific), nor does it specify prerequisites or appropriate contexts for music cost estimation versus other estimation 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?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the cost ($1.96 for Veo 3.1) but doesn't explain what this tool actually does behaviorally - whether it performs calculations, looks up pricing, or returns static information. It doesn't disclose rate limits, authentication needs, or what happens when invoked.
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 extremely concise - just one sentence. While efficient, it may be too brief given the lack of annotations and sibling tool context. Every word earns its place, but more information might be warranted for this type of tool.
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?
For a cost estimation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns, how the cost calculation works, or what the $1.96 baseline represents. Given the complexity of cost estimation with multiple parameters and the lack of structured output documentation, more completeness is needed.
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 the schema already fully documents all three parameters. The description adds no parameter-specific information beyond what's in the schema. The baseline score of 3 reflects adequate but minimal value addition given the comprehensive schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: estimating cost for video generation with a specific price point. It specifies the resource (video generation) and verb (estimate cost), but doesn't differentiate from sibling 'estimate_cost' or 'estimate_music_cost' tools, which appear to be similar estimation tools for different resources.
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 like 'estimate_cost' or 'estimate_music_cost'. It doesn't mention prerequisites, limitations, or appropriate contexts for use. The only contextual information is the price point, which doesn't help with tool selection decisions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool returns 'output URL when completed', which implies it's a polling/status-checking operation, but doesn't describe error handling, timeout behavior, rate limits, or what happens if the operation is still in progress.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that directly state the tool's purpose and key behavioral detail. It's front-loaded with the main function and follows with the completion behavior, though it could be slightly more structured.
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?
For a status-checking tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'status' means (e.g., pending, running, completed, failed), what format the 'result' takes, or how to interpret the output URL. Given the complexity of operation monitoring, more context is needed.
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 the schema already fully documents the single parameter. The description adds minimal value by mentioning the operation_id comes 'from an operation tool', but doesn't provide additional context about format, validation, or example values beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('status and result of an operation'), and mentions the output URL when completed. However, it doesn't explicitly differentiate this tool from its sibling 'get_task', which appears to serve a similar monitoring function.
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 like 'get_task' or 'list_tasks'. It mentions the operation_id comes 'from an operation tool', but doesn't specify which sibling tools qualify as operation tools or when polling is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions 'recent' tasks but doesn't define what 'recent' means (time window, pagination). No information about permissions, rate limits, error handling, or what the output looks like (structure, fields).
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, efficient sentence with zero wasted words. It's front-loaded with the core purpose and includes the key optional feature. Every word earns its place.
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?
For a list operation with no annotations and no output schema, the description is insufficient. It doesn't explain what 'recent' means, the output format, pagination behavior, or error cases. Given the complexity of task management systems, more context is needed for effective 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 the schema fully documents both parameters. The description adds no additional meaning beyond implying filtering by status is optional, which is already clear from the schema's optional status parameter. Baseline 3 is appropriate when schema does the heavy lifting.
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 ('List') and resource ('recent generation tasks') with an optional filter. It distinguishes from siblings like 'get_task' (singular) by indicating it returns multiple tasks, but doesn't explicitly contrast with other list-like operations that might exist.
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. It doesn't mention prerequisites, appropriate contexts, or compare with sibling tools like 'get_task' for single-task retrieval or 'get_operation' which might have overlapping functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the cost ('$0.18') but doesn't specify if this is per image, per request, or how it varies with parameters. No information about rate limits, authentication needs, or what happens when invoked is provided.
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 brief and front-loaded with the core purpose. However, the cost information ('Cost: $0.18') could be better integrated or explained, making it slightly less than perfectly structured.
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?
For a cost estimation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., cost breakdown, total estimate) or how the cost calculation works with the parameters, leaving significant gaps for agent understanding.
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 the schema fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: estimating cost before image generation execution. It specifies the resource (image generation) and verb (estimate cost), but doesn't distinguish from sibling tools like 'estimate_music_cost' or 'estimate_video_cost' beyond mentioning 'image' in the description.
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 context ('before executing') suggesting this should be used prior to actual generation, but doesn't explicitly state when to use this versus alternatives like 'generate_image' or provide exclusion criteria. The context is implied rather than explicitly defined.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns balance information, implying a read-only operation, but does not address potential behavioral traits such as authentication needs, rate limits, error conditions, or side effects. This leaves significant gaps in understanding how the tool behaves.
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 highly concise and front-loaded, consisting of two sentences that directly state the tool's function and output. Every sentence earns its place with no wasted words, making it efficient and easy to parse.
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 simplicity (0 parameters, no output schema, no annotations), the description is minimally complete. It covers the basic purpose and return values but lacks details on behavioral context, usage guidelines, and output structure, which could be beneficial for an agent despite the 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?
The input schema has 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description does not add parameter details, but since there are no parameters, this is acceptable. It provides baseline clarity by implying no inputs are needed, aligning with the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as checking a VAP account balance and specifies what it returns (available, reserved, and usable balances). It uses a specific verb ('Check') and resource ('VAP account balance'), but it does not distinguish itself from sibling tools, which are unrelated to account management (e.g., image generation, video editing).
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. It does not mention any prerequisites, context for usage, or exclusions, leaving the agent without direction on its appropriate application relative to other 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?
No annotations are provided, so the description carries the full burden. It discloses cost ('Cost: $0.15') and a prerequisite ('Requires Tier 1+'), which are useful behavioral traits. However, it lacks details on rate limits, error handling, or what the output looks like (since no output schema exists). This adds some value but is incomplete for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the core purpose. Every sentence adds value: the first states the action, the second adds capability details, and the third provides cost and prerequisites. It could be slightly more structured but is efficient with zero waste.
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 annotations and no output schema, the description is moderately complete. It covers purpose, cost, and prerequisites but lacks details on behavioral aspects like rate limits or output format. For an AI editing tool with 5 parameters and no structured output, it should do more to compensate for the missing annotations, making it adequate but with 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%, so the schema already documents all 5 parameters well. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain 'prompt' formats or 'additional_images' use cases). Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Edit images using AI with natural language prompts.' It specifies the resource (images) and the action (edit with AI/prompts). However, it does not explicitly differentiate from sibling tools like 'inpaint' or 'background_remove,' which might also involve image editing, so it lacks sibling differentiation for a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage context: 'Supports single and multi-image editing' and 'Requires Tier 1+.' It implies when to use this tool (for AI-based image editing with prompts) but does not explicitly state when not to use it or name alternatives among siblings (e.g., vs. 'inpaint' or 'background_remove'). This is adequate but has clear gaps in guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'image URL when completed,' which adds useful context about output behavior. However, it doesn't cover other behavioral traits like error handling, polling requirements, or rate limits. The description is adequate but lacks depth for a mutation-free status check 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 and front-loaded: two sentences that directly state the tool's purpose and key output. Every word earns its place, with no redundant or vague phrasing. It efficiently communicates essential information without unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single parameter, no annotations, no output schema), the description is reasonably complete. It covers the basic purpose and output hint ('image URL when completed'). However, it could better address behavioral aspects like task states (e.g., pending, failed) or error cases, which would enhance completeness for a status-checking 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 description coverage is 100%, with the single parameter 'task_id' well-documented in the schema. The description adds minimal value by referencing 'task_id returned from generate_image,' which clarifies the parameter's origin but doesn't provide additional semantics beyond the schema. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get the status and result of a generation task.' It specifies the verb ('Get') and resource ('generation task'), and distinguishes it from siblings like 'generate_image' (which creates tasks) and 'list_tasks' (which lists multiple tasks). However, it doesn't explicitly differentiate from 'get_operation' (another status-checking tool), keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning 'task_id returned from generate_image,' suggesting it's used after task creation. However, it lacks explicit guidance on when to use this tool versus alternatives like 'list_tasks' for bulk status checks or 'get_operation' for other operations. No exclusions or prerequisites are stated, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and adds valuable behavioral context: it discloses the monetary cost ($0.15) and access requirements (Tier 1+), which are crucial for usage decisions. However, it doesn't mention rate limits, response format, or error conditions.
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 with just two sentences that each earn their place: the first explains the core functionality, and the second provides critical operational constraints (cost and requirements). No wasted words or redundant 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?
For a mutation tool with no annotations and no output schema, the description covers the basic purpose and constraints well but lacks information about what the tool returns (e.g., edited image URL, status) and potential side effects. Given the complexity of image editing, more output information would be helpful.
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 the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for high 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's purpose with specific verbs ('remove or replace objects') and identifies the resource ('image') and method ('AI inpainting'). It distinguishes from siblings like 'background_remove' (specific background removal) and 'ai_edit' (broader editing) by focusing on object-level inpainting.
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 object removal/replacement in images, but doesn't explicitly state when to use this versus alternatives like 'background_remove' (for backgrounds only) or 'ai_edit' (for general edits). It mentions cost and tier requirements, which provide some context but not comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively adds context beyond the input schema: it specifies the AI model (VAP/Flux2 Pro), reveals async behavior (returns a task ID for tracking), and discloses cost ($0.18). However, it omits details like rate limits, authentication needs, or error handling, keeping it from a perfect score.
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 highly concise and front-loaded, with two sentences that efficiently cover purpose, technology, async behavior, and cost. Every element adds value without redundancy, making it easy for an agent to quickly grasp the tool's essence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, async operation, cost) and lack of annotations/output schema, the description is largely complete. It covers key behavioral aspects like async tracking and cost, but could improve by mentioning output format (e.g., image URL or file type) or error scenarios, slightly limiting 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 description coverage is 100%, providing detailed parameter documentation. The description adds minimal semantics beyond the schema, only mentioning cost implications for quality indirectly. It doesn't elaborate on prompt best practices or aspect ratio interactions beyond what's in the schema descriptions, so it meets the baseline for high 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 specific action ('Generate an AI image'), resource ('from text prompt'), and technology ('using VAP (Flux2 Pro)'), distinguishing it from siblings like generate_music or generate_video. It precisely communicates the core function without being vague or tautological.
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 AI image generation but provides no explicit guidance on when to use this tool versus alternatives like ai_edit or inpaint. It mentions async tracking via task ID, which hints at use cases requiring follow-up, but lacks clear when/when-not directives or named alternatives for similar tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively adds critical context beyond the schema: the cost ($0.05) and access requirement (Tier 1+), which are essential for an agent to understand usage constraints. However, it doesn't mention other behavioral traits like rate limits, output format, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of just two sentences that directly state the tool's purpose and key constraints (cost and tier). Every word earns its place, with no redundant or vague language, making it highly 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 the tool's moderate complexity (merging videos with cost and access constraints), no annotations, and no output schema, the description does well by covering the core purpose and key behavioral aspects. However, it lacks details on output (e.g., format, size) and error cases, leaving some gaps for the agent to infer or handle unexpectedly.
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 the schema already documents the single parameter (media_urls) with its type, description, and constraints. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate but no extra 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 specific action ('merge multiple video clips') and resource ('into one continuous video'), distinguishing it from sibling tools like video_trim (which edits rather than combines) and generate_video (which creates new content). It precisely communicates the tool's function without ambiguity.
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 through the mention of cost and tier requirements, suggesting it's for paid operations, but doesn't explicitly state when to use this versus alternatives like video_trim or generate_video. No guidance is provided on prerequisites beyond the tier requirement, leaving the agent to infer context from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively adds context beyond the input schema by revealing cost implications and access requirements, though it lacks details on rate limits, error handling, or processing time.
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 highly concise and front-loaded, consisting of two sentences that directly state the tool's purpose, outcome, cost, and requirements without any redundant 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?
For a tool with no annotations and no output schema, the description adequately covers the core functionality and prerequisites. However, it lacks information on output format (e.g., image type, transparency handling) and error cases, which would be beneficial given the absence of structured output documentation.
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 the single parameter 'media_url' documented as 'URL of the image to process'. The description does not add further parameter details beyond what the schema provides, meeting the baseline for high 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 specific action ('Remove the background from an image') and the outcome ('leaving only the subject with transparency'), distinguishing it from sibling tools like 'inpaint' or 'upscale' that perform different image operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for usage by specifying cost and tier requirements ('Cost: $0.10. Requires Tier 1+'), but does not explicitly state when to use this tool versus alternatives like 'ai_edit' or 'inpaint' for other image modifications.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively adds valuable context beyond what the input schema provides: it discloses the cost ($0.68), mentions async tracking with a task ID, provides important usage constraints ('Send ONLY the music description'), and gives guidance on what to include in prompts. It doesn't cover rate limits or authentication needs, but provides substantial 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose. Every sentence earns its place: the first states the purpose and cost, the second provides critical usage instruction, the third gives prompt guidance, and the fourth provides an example. Minor formatting issues prevent a perfect score.
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 music generation tool with 5 parameters and no annotations or output schema, the description does a good job of providing necessary context. It covers the purpose, cost, async nature, usage constraints, and prompt guidance. The main gap is lack of information about what the tool returns (beyond mentioning a task ID) and error handling.
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 adds some parameter semantics by providing an example prompt that illustrates the 'prompt' parameter usage and mentioning vocal style/instrumental options. However, with 100% schema description coverage, the schema already documents all 5 parameters thoroughly. The description provides marginal additional value beyond what's in the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate AI music from text description using VAP (Suno V5).' It specifies the verb ('Generate'), resource ('AI music'), and technology used ('VAP (Suno V5)'), distinguishing it from sibling tools like generate_image 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use this tool by specifying it's for generating music from text descriptions and giving an example prompt. However, it doesn't explicitly mention when NOT to use it or name specific alternatives among siblings (e.g., when to use generate_video instead).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively communicates key behavioral traits: the asynchronous nature ('Returns a task ID for async tracking'), cost implications ('Cost: $1.96'), and strict input requirements ('IMPORTANT: Send ONLY the video description...'). It doesn't mention rate limits, authentication needs, or error conditions, but provides substantial operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly front-loaded with the core purpose, followed by critical behavioral information (async tracking, cost), then essential usage instructions. Every sentence earns its place with zero waste. The structure moves from general to specific in a logical flow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex video generation tool with 6 parameters and no annotations or output schema, the description provides substantial context about the operation's nature, cost, and input requirements. It effectively compensates for the lack of output schema by explaining the async task ID return. However, it doesn't address potential failure modes, quality expectations, or integration with sibling tools like get_task for tracking.
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 the schema already documents all 6 parameters thoroughly. The description reinforces the critical constraint for the 'prompt' parameter ('Send ONLY the video description...'), adding some semantic emphasis beyond the schema. However, it doesn't provide additional meaning for other parameters beyond what's already in their schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Generate an AI video from text prompt') and resource ('using VAP (Veo 3.1)'), distinguishing it from sibling tools like generate_image or generate_music. It provides a complete functional statement beyond just restating the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('Generate an AI video from text prompt') and includes important usage instructions ('Send ONLY the video description...'). However, it doesn't explicitly differentiate when to choose this over alternatives like video_merge or video_trim, nor does it mention prerequisites like checking balance first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and adds valuable behavioral context: it discloses the cost ('$0.15') and access requirement ('Requires Tier 1+'), which are not inferable from the schema. However, it lacks details on rate limits, output format, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by cost and access details in two concise sentences. Every element earns its place without redundancy or 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?
Given the tool's moderate complexity (AI-based image processing), no annotations, and no output schema, the description is reasonably complete: it covers purpose, cost, and access. However, it omits details on output (e.g., format, size) and potential limitations (e.g., supported image types).
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 the schema fully documents both parameters. The description does not add meaning beyond the schema (e.g., it doesn't explain URL requirements or scale implications), meeting the baseline for high 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 specific action ('upscale/enhance an image to higher resolution using AI'), identifies the resource ('an image'), and distinguishes from siblings by focusing on resolution enhancement rather than editing, generation, or other media operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('upscale/enhance an image'), but does not explicitly state when not to use it or name alternatives among siblings (e.g., 'ai_edit' for other enhancements). The cost and tier requirement offer practical constraints.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively adds important context beyond basic functionality: it discloses monetary cost ($0.05) and access restrictions (Tier 1+ requirement). While it doesn't mention rate limits, processing time, or output format, it provides crucial operational constraints for a paid service.
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 short sentences) and front-loaded with the core functionality first, followed by operational constraints. Every word earns its place with no redundant information or 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 mutation tool with no annotations and no output schema, the description does well by including cost and access requirements. However, it doesn't describe what happens after trimming (e.g., where the trimmed video is stored, format preservation, or how to retrieve results), leaving some gaps in operational understanding.
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 all three parameters clearly documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema (like format details for media_url or validation rules for time values), so it meets the baseline for high 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 specific action ('trim') and resource ('video'), specifying the operation is limited to 'a specific time range'. It distinguishes from sibling tools like video_merge (which combines videos) and ai_edit (which might involve more complex editing).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool by mentioning cost and tier requirements ('Cost: $0.05. Requires Tier 1+'), which helps determine feasibility. However, it doesn't explicitly state when NOT to use it or name alternatives for similar video editing tasks among siblings.
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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