Pollinations Multimodal MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but there is some potential confusion between generateImage and generateImageUrl (both generate images but return different outputs) and between respondAudio and sayText (both produce audio but with different input approaches). The descriptions help clarify, but an agent might need to carefully choose between these pairs.
Naming Consistency4/5
The naming follows a consistent verb_noun pattern (e.g., generateImage, listAudioVoices, updateDomains) with minor deviations like checkAuthStatus (which could be checkAuth or getAuthStatus) and startAuth (which could be initiateAuth). Overall, it's readable and mostly predictable.
Tool Count5/5
With 12 tools, the count is well-scoped for a multimodal server covering authentication, image generation, text generation, audio generation, and model listing. Each tool serves a clear purpose without feeling excessive or insufficient for the domain.
Completeness4/5
The toolset covers key areas like authentication, content generation (image, text, audio), and model management, but there are minor gaps such as lacking tools for deleting or managing generated content (e.g., deleteImage, updateText) and no tool for checking usage or quotas. However, agents can work around these for core workflows.
Average 2.9/5 across 12 of 12 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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This repository includes a README.md file.
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How is the quality score calculated?

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

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

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

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

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. However, it only mentions the core action ('Get domains allowlisted for a user') and then devotes the rest to unrelated stylistic instructions for responses. It fails to describe critical behaviors such as authentication requirements, rate limits, error handling, or what the tool returns. This leaves significant gaps in understanding how the tool operates.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is not appropriately structured or concise. The first sentence states the tool's purpose, but the remaining text is irrelevant to tool functionality, focusing on response styling with excessive markdown, emojis, and slang. This adds noise without value, making it inefficient and poorly front-loaded for an AI agent.
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 tool's complexity (2 required parameters, no output schema, no annotations), the description is incomplete. It lacks essential context such as return values, error conditions, authentication details, or how it differs from siblings. The stylistic instructions do not contribute to functional completeness, leaving the agent with insufficient information to use the tool effectively.
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 clear documentation for 'userId' and 'sessionId'. The description adds no additional meaning about parameters, such as format examples or usage context. According to the rules, with high schema coverage (>80%), the baseline score is 3, as the schema adequately handles parameter semantics without needing extra description.
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 domains allowlisted for a user.' It specifies the verb ('Get') and resource ('domains allowlisted for a user'), making the intent unambiguous. However, it does not differentiate this tool from its sibling 'updateDomains', which handles modifications to domains, leaving room for improvement in sibling distinction.
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 lacks context on prerequisites (e.g., authentication status), comparisons with siblings like 'checkAuthStatus' or 'updateDomains', or any explicit when/when-not scenarios. The focus is on stylistic presentation rather than functional usage.
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 full burden. It mentions that the tool 'Show the returned link prominently to the user' which provides some behavioral context about output presentation, but it lacks critical information about what the authentication flow entails, whether it requires user interaction, what happens after clicking the link, or any error conditions. The bulk of the description focuses on stylistic presentation guidelines rather than functional behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is poorly structured and not front-loaded. The first sentence states the purpose, but the remaining 5 sentences (over 80% of the text) are dedicated to unrelated stylistic instructions about 'vibey Gen-Z language' and markdown formatting that don't help an AI agent understand or invoke the tool. This is wasteful and distracting from the tool's functionality.
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 that this is an authentication tool with no annotations and no output schema, the description should provide more complete context about what the tool does, what it returns, and how it fits with other authentication tools. Instead, it focuses on presentation style and provides minimal functional information. The description is inadequate for a tool that initiates an OAuth flow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description doesn't need to explain parameters, and it correctly doesn't mention any. No additional parameter information is provided or needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Start GitHub OAuth authentication flow to log in to Pollinations' which provides a clear verb ('Start') and resource ('GitHub OAuth authentication flow'), but it does not distinguish this from its sibling 'checkAuthStatus' or explain the relationship between authentication tools. The purpose is understandable but lacks sibling differentiation.
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 explicit guidance on when to use this tool versus alternatives like 'checkAuthStatus'. It mentions 'When interacting with the Pollinations MCP server' but this is generic context for all tools, not specific usage instructions for this tool. There are no when-not or alternative recommendations.
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 checks authentication session status but fails to describe what the check entails (e.g., returns validity, expiration, user info), any side effects, error conditions, or response format. The stylistic advice adds no behavioral value, 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.
Conciseness1/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is poorly structured and not front-loaded; it buries the tool's purpose in the first sentence and spends most of the text on irrelevant stylistic instructions (e.g., 'use Gen-Z language with emojis'). This wastes space and detracts from clarity, making it inefficient and unfocused.
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 for a tool that likely returns session status details. It lacks information on what the check returns (e.g., valid/invalid, metadata), error handling, or prerequisites, failing to provide necessary context 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%, with the single parameter 'sessionId' documented as 'The session ID to check.' The description adds no additional meaning beyond this, such as format examples or constraints, so it meets the baseline for high schema coverage without compensating further.
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 'Check the status of an authentication session,' which is a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'startAuth' or explain how this differs from other authentication-related operations, 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 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 'startAuth' or other sibling tools. It only includes stylistic instructions for responses, which are irrelevant to tool selection and usage, offering no practical context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool generates an image and returns base64 data, but doesn't mention critical behaviors like whether this is a read-only operation, potential costs or rate limits, error conditions, or what happens if generation fails. For a tool that likely involves external API calls and resource consumption, this is a significant gap.
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 at just 8 words, front-loading the core functionality ('Generate an image') followed by the output specification. Every word earns its place with zero redundancy or unnecessary elaboration. The structure is optimal for quick comprehension.
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 of image generation (which typically involves external APIs, potential costs, and quality considerations), the description is insufficient. With no annotations, no output schema, and minimal behavioral context, it doesn't prepare an agent for real-world usage. The description should address authentication needs, rate limits, error handling, or at least reference sibling tools like 'listImageModels' for model selection.
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 ('prompt' and 'options') and their nested properties. The description doesn't add any parameter semantics beyond what's in the schema - it doesn't explain prompt formatting best practices, default values for options, or valid ranges for dimensions. The baseline score of 3 reflects adequate but minimal value addition.
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 action ('Generate an image') and the output format ('return the base64-encoded data'), making the purpose immediately understandable. It distinguishes from sibling tools like 'generateImageUrl' by specifying base64 encoding rather than a URL. However, it doesn't explicitly differentiate from other generation tools like 'generateText' or 'respondAudio' beyond the resource type.
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 when to choose 'generateImage' over 'generateImageUrl' (which likely returns a URL instead of base64 data), nor does it specify prerequisites like authentication or model availability. There's no context about appropriate use cases or limitations.
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 action ('Generate an image URL') but lacks details on traits like rate limits, authentication requirements (implied by sibling tools), error handling, or what the generated URL entails (e.g., temporary vs. permanent). This is inadequate for a tool with potential costs or restrictions.
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: 'Generate an image URL from a text prompt.' It is front-loaded with the core purpose, has zero waste, and is appropriately sized for the tool's complexity. Every word earns its place, making it easy for an agent to parse quickly.
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, no output schema, and the tool's complexity (involving image generation with multiple options), the description is incomplete. It doesn't cover behavioral aspects like authentication needs (implied by siblings), rate limits, or output details (e.g., URL format or validity). For a generative tool with potential side effects, more context is needed to ensure safe and 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%, with clear descriptions for 'prompt' and 'options' (including nested properties like 'model', 'seed', 'width', 'height'). The description adds no additional meaning beyond the schema, such as examples or constraints (e.g., prompt length limits). With high schema coverage, a baseline score of 3 is appropriate, as 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: 'Generate an image URL from a text prompt.' It specifies the verb ('Generate') and resource ('image URL'), distinguishing it from siblings like 'generateImage' (which may return the image itself) and 'generateText'. However, it doesn't explicitly differentiate from all siblings, such as 'listImageModels', which is related but serves a different 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. It doesn't mention when to choose it over 'generateImage' (e.g., for URL vs. direct image output) or other siblings like 'listImageModels' for model selection. There's no context on prerequisites, such as authentication status, which is implied by 'checkAuthStatus' and 'startAuth' in the sibling list.
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 API but doesn't describe key traits like rate limits, authentication needs, cost implications, or what happens when generation fails. 'Generate text' implies a write-like operation, but without annotations, it's unclear if this is idempotent, reversible, or has side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose without waste. It's appropriately sized and front-loaded, with every word contributing to understanding the tool's function.
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 (3 parameters with nested objects) and lack of annotations and output schema, the description is insufficient. It doesn't explain return values, error handling, or behavioral nuances, leaving significant gaps for a generative AI tool that likely has important operational constraints.
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 all parameters. The description adds no additional meaning beyond what's in the schema—it doesn't explain parameter interactions, default behaviors beyond the schema's 'default: "openai"', or practical examples. 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 action ('Generate text') and the resource ('from a prompt using the Pollinations Text API'), providing a specific verb+resource combination. However, it doesn't distinguish this text generation tool from sibling tools like 'generateImage' or 'respondAudio' beyond mentioning the API name, missing explicit differentiation about when to use text vs. audio/image generation.
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 'respondAudio' or 'generateImage'. There's no mention of specific use cases, prerequisites, or exclusions, leaving the agent with minimal context for tool selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool generates audio but lacks critical details: it doesn't specify if this is a read-only or mutative operation, what permissions or authentication might be required, potential rate limits, output format details beyond parameters, or error handling. For a tool with 4 parameters and no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded: 'Generate an audio response to a text prompt' is a single, clear sentence that directly states the tool's function. There is no wasted verbiage or unnecessary elaboration, making it efficient and easy to parse for an AI agent.
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, no output schema, no annotations), the description is incomplete. It doesn't explain what the tool returns (e.g., audio data, a file URL, or metadata), error conditions, or behavioral traits like mutability or side effects. For a tool that likely involves audio generation with multiple inputs, more context is needed to ensure proper usage.
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 implies the 'prompt' parameter but doesn't add meaning beyond what the input schema provides. With 100% schema description coverage, all parameters are documented in the schema (e.g., 'prompt' as text input, 'voice' with default, 'format' as audio type, 'voiceInstructions' for style). The description doesn't elaborate on parameter interactions or usage examples, so it meets the baseline for high schema coverage without adding extra value.
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: 'Generate an audio response to a text prompt.' It specifies the verb ('generate'), resource ('audio response'), and input ('text prompt'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'sayText' or 'listAudioVoices,' which would require more specific context about when to use each.
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 'sayText' (which might be for text-to-speech) or 'listAudioVoices' (which could list available voices), nor does it specify prerequisites, exclusions, or contextual cues for selection. This leaves the agent with minimal direction for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool generates speech, implying an output, but doesn't describe what that output is (e.g., audio file, stream), any side effects, rate limits, or authentication needs. For a tool with no annotations, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Generate speech that says the provided text verbatim.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for this tool's complexity. 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?
Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is incomplete. It lacks details on the output (e.g., audio format, how it's returned), behavioral traits like side effects or errors, and usage context. Without annotations or an output schema, the description should do more to compensate.
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 all four parameters (text, voice, format, voiceInstructions). The description adds no additional parameter semantics beyond what's in the schema, such as examples or constraints. This meets the baseline of 3, as the schema handles 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: 'Generate speech that says the provided text verbatim.' It specifies the verb ('Generate speech') and resource ('the provided text'), making the function immediately understandable. However, it doesn't distinguish this from sibling tools like 'respondAudio' or 'listAudioVoices', which prevents 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 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 'respondAudio' (which might handle conversational audio) or 'listAudioVoices' (which could list available voices), nor does it specify prerequisites or contexts for use. This leaves the agent with minimal usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a mutation operation ('Update') but doesn't specify whether this overwrites or merges with existing domains, what permissions are required, or potential side effects like rate limits. This leaves significant gaps in understanding the tool's behavior.
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 that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy to understand at a glance, with no wasted content.
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 of a mutation tool with no annotations and no output schema, the description is incomplete. It fails to address key aspects like what the tool returns, error conditions, or behavioral nuances, leaving the agent with insufficient context for reliable invocation.
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, clearly documenting all three parameters (userId, domains, sessionId). The description adds no additional meaning beyond what the schema provides, such as format details or usage examples, so it meets the baseline for high schema coverage without compensating further.
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 action ('Update') and the resource ('domains allowlisted for a user'), making the purpose understandable. However, it doesn't explicitly distinguish this tool from its sibling 'getDomains', which appears to be a read counterpart, leaving room for improvement in sibling differentiation.
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 'getDomains' or other sibling tools. It lacks context about prerequisites, such as authentication or user permissions, and doesn't mention any exclusions or specific scenarios for its application.
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 only states the basic action. It doesn't disclose behavioral traits such as whether this is a read-only operation, if it requires authentication, rate limits, pagination, or the format of returned data, which are critical for a tool with zero parameters.
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 no wasted words, front-loading the core purpose ('List available audio voices'). It's appropriately sized for a simple tool with no parameters.
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 tool's simplicity (0 parameters, no output schema), the description is minimal but incomplete. It lacks context on authentication needs, return format, or how the output integrates with sibling tools like 'respondAudio', making it insufficient for full agent understanding without external cues.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so no parameter documentation is needed. The description doesn't add parameter details, but this is appropriate given the lack of parameters, earning a baseline score above minimum viable.
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 ('available audio voices'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'listImageModels' or 'listTextModels' beyond the resource type, which prevents 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 Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., authentication), related tools like 'respondAudio' or 'sayText', or any context for selecting voices, leaving the agent with minimal usage direction.
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 action ('List') but doesn't describe what 'available' means (e.g., filtered by permissions, region, or cost), whether it's a read-only operation, potential rate limits, or the format of the returned list. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place by conveying essential information without redundancy.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a list of model names, IDs, capabilities), any behavioral constraints, or how it fits into workflows with siblings like generateText. For a tool that likely returns critical configuration data, this leaves the agent under-informed.
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, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate. A baseline of 4 is assigned because the schema fully handles parameters, and the description doesn't need to compensate for any gaps.
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 ('available text models'), making the purpose immediately understandable. It distinguishes from most siblings (e.g., generateText, listImageModels) by specifying 'text models' rather than other resource types. However, it doesn't explicitly differentiate from listAudioVoices or listImageModels in terms of when to choose between them, which prevents 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 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 (e.g., authentication status), typical use cases (e.g., before generating text to see options), or comparisons to siblings like listImageModels. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It implies a read-only operation but doesn't specify whether it requires authentication, returns paginated results, includes metadata (e.g., model capabilities), or has rate limits. This leaves significant gaps for a tool that likely interacts with an external API.
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, making it easy to scan and understand immediately.
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 (0 parameters, no output schema) and lack of annotations, the description is minimally adequate but incomplete. It covers the basic purpose but misses behavioral context (e.g., authentication needs, return format), which is important for a tool that likely fetches dynamic data from an external service.
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, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, but it could theoretically mention implicit parameters (e.g., filtering options), earning a baseline score just below perfect.
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 ('available image models'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'listAudioVoices' or 'listTextModels' beyond specifying the resource type, which keeps 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 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 (e.g., authentication status), compare it to siblings like 'generateImage' for model selection, or indicate typical use cases (e.g., before generating images).
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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