MapleFlow

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=true, which the description does not contradict (it describes sending and receiving messages, not modifying data). The description adds useful context beyond annotations by mentioning support for '30+ AI models' and 'conversation history via the messages array', but it lacks details on behavioral traits like rate limits, authentication needs, or response formats. With annotations covering safety, this is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, with two sentences that efficiently convey the tool's purpose and key features ('Send a message to any of 30+ AI models', 'Returns the model's response', 'Supports conversation history'). Every sentence earns its place without waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (chat with multiple AI models), rich schema coverage (100%), and annotations (readOnlyHint=true), the description is mostly complete. It covers the core functionality and context well. However, the lack of an output schema means the description could benefit from more detail on return values (e.g., response structure), slightly reducing completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 parameters (model, messages, max_tokens) thoroughly. The description adds minimal value beyond the schema by implying the 'messages' parameter supports conversation history, but it does not provide additional syntax or format details. 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.

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 ('Send a message', 'Returns the model's response') and resources ('30+ AI models', 'conversation history via the messages array'). It distinguishes itself from siblings like ai_embeddings, ai_images, etc., by focusing on chat interactions rather than embeddings, image generation, or other AI tasks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 ('Send a message to any of 30+ AI models'), implying it's for chat-based AI interactions. However, it does not explicitly state when not to use it or name alternatives among siblings (e.g., use ai_embeddings for vector generation instead), which prevents a perfect score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations declare readOnlyHint=true, indicating this is a safe read operation. The description adds context about the tool's purpose (generating embeddings for search/comparison), which goes beyond the annotation's safety profile. However, it doesn't disclose other behavioral traits like performance characteristics, rate limits, or model specifics. No contradiction with annotations exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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's front-loaded with the core function and includes the application context concisely. Every part of the sentence earns its place by contributing essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (generating embeddings), the description covers the basic purpose adequately. However, with no output schema and only readOnlyHint annotations, it lacks details on return values (e.g., embedding format, dimensions) and doesn't address potential limitations or error cases. The description is minimally viable but has clear gaps for an AI tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the 'text' parameter fully documented. The description doesn't add any parameter-specific details beyond what the schema provides (e.g., text format requirements, length limits, or batch processing hints). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 vector embeddings for text search and similarity comparison.' It specifies the verb ('generate') and resource ('vector embeddings'), and provides a clear application context. However, it doesn't explicitly differentiate from sibling tools like 'ai_chat' or 'ai_transcribe' that might also process text, 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.

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 mentions 'text search and similarity comparison' as applications, but doesn't specify when to choose this over other AI tools like 'ai_chat' for text processing or 'translate' for language tasks. There are no explicit when/when-not instructions or named alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true, indicating a safe operation, which the description doesn't contradict. The description adds value by specifying the return format ('base64-encoded PNG') and implying image generation, but doesn't cover rate limits, quality trade-offs, or error conditions beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with two sentences that front-load the core purpose and output format, with zero wasted words. Every sentence earns its place by providing essential information efficiently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (image generation with parameters), no output schema, and annotations covering safety, the description is adequate but incomplete. It explains the basic operation and output format but lacks details on performance, limitations, or integration context that would enhance agent understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

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 parameters like 'prompt' and 'num_steps'. The description adds no additional parameter semantics beyond what's in the schema, but doesn't need to compensate for gaps, meeting the baseline for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Generate images from text prompts') and resource ('using Flux models'), distinguishing it from sibling tools like ai_chat or ai_transcribe that handle different AI tasks. It provides a complete picture of what the tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description offers no guidance on when to use this tool versus alternatives like ai_chat for text generation or other image tools not listed. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=true, which the description doesn't contradict (it describes a conversion process, not a write operation). The description adds useful context beyond annotations by specifying the output format ('base64-encoded WAV'), but it doesn't disclose other behavioral traits like rate limits, quality settings, or language support that might be relevant for an AI agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded, consisting of two clear sentences that directly state the tool's function and output. There is no wasted language, and every sentence earns its place by providing essential information without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (text-to-speech conversion), the description is minimally complete. It covers the purpose and output format, but with no output schema, it doesn't explain return values in detail (e.g., audio duration or error handling). The annotations provide safety info, but the description could benefit from more context on limitations or use cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with the single parameter 'text' fully documented in the schema. The description doesn't add any meaning beyond the schema (e.g., it doesn't specify text length limits or language requirements). Baseline 3 is appropriate since the schema handles parameter documentation adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Convert text to natural-sounding speech audio') and the resource ('text'), distinguishing it from siblings like ai_chat or ai_transcribe which handle different AI tasks. It precisely defines what the tool does 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.

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 ai_transcribe (which might handle speech-to-text) or other audio-related tools, nor does it specify contexts or exclusions for usage. The agent must infer usage from the purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations indicate readOnlyHint=true, confirming this is a read operation. The description adds context by specifying 'with timestamps' and 'using Whisper', which clarifies the output format and underlying technology. However, it doesn't disclose rate limits, quality expectations, or other behavioral traits beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose. It avoids redundancy and wastes no words, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (audio transcription), lack of output schema, and annotations covering only read-only status, the description is minimally adequate. It specifies timestamps and Whisper, but doesn't explain return values, error conditions, or integration details, leaving gaps for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with the parameter 'audio' fully documented as 'Base64-encoded audio file'. The description doesn't add meaning beyond this, such as audio format requirements or size limits, 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.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Transcribe audio to text with timestamps using Whisper.' It specifies the verb (transcribe), resource (audio), and key feature (timestamps). However, it doesn't explicitly differentiate from sibling tools like 'ai_speech', which might handle similar audio processing tasks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 this over sibling tools like 'ai_speech' or other transcription methods, nor does it specify prerequisites or exclusions for usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide readOnlyHint=true, indicating a safe read operation. The description adds value by specifying 'live' exchange rates (implying real-time data) and the scope of '160+ currencies', which are useful behavioral traits not covered by annotations. However, it doesn't disclose rate limits, data sources, or error handling, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core functionality ('Get live currency exchange rates') and adds supplementary detail ('convert between 160+ currencies') without any wasted words. Every part of the sentence contributes to understanding the tool's purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (currency conversion with real-time data), annotations cover safety (read-only), and schema fully documents parameters, the description is adequate but incomplete. It lacks output details (no schema provided), error cases, or examples, which could help an agent use it correctly in varied contexts.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with clear documentation for all three parameters (to, from, amount). The description adds minimal semantics beyond the schema, mentioning 'convert between 160+ currencies' which hints at the parameter usage but doesn't provide additional syntax or format details. Baseline 3 is appropriate given the comprehensive schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 ('Get live currency exchange rates' and 'convert between 160+ currencies'), identifies the resource (currency exchange rates), and distinguishes it from all sibling tools which are AI-related or other services like weather and translation. No tautology or vagueness present.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 any prerequisites, exclusions, or contextual factors that would help an agent decide between this and other tools (e.g., using 'translate' for language conversion vs. this for currency conversion). Usage is implied but not explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide readOnlyHint=true, indicating it's a safe read operation. The description adds valuable behavioral context beyond this: it discloses 'auto-detection' capability and 'dynamic pricing based on text length,' which are not covered by annotations. This helps the agent understand cost implications and automatic features, though it doesn't detail rate limits 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is highly concise and front-loaded with essential information in two sentences. The first sentence covers the core purpose and key features (75+ languages, auto-detection), and the second adds important behavioral detail (dynamic pricing). Every sentence earns its place with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (translation with auto-detection and pricing), annotations cover safety, and schema fully documents parameters. The description adds useful context like language count and pricing model. However, without an output schema, it doesn't explain return values (e.g., translated text format), leaving a minor gap. Overall, it's mostly complete for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds minimal semantic value beyond the schema: it mentions 'auto-detection' which relates to the 'from' parameter's 'auto' option, but doesn't explain parameter interactions or provide additional meaning. 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.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Translate text between 75+ languages with auto-detection.' It specifies the verb (translate) and resource (text), but doesn't explicitly distinguish it from sibling tools like 'ai_chat' or 'ai_transcribe' which might also involve language processing. The mention of '75+ languages' adds specificity, but sibling differentiation is implied rather than explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context through 'auto-detection' and 'dynamic pricing based on text length,' suggesting when to use it for translation tasks. However, it doesn't provide explicit guidance on when to choose this tool over alternatives like 'ai_chat' for language-related queries or when not to use it (e.g., for simple word lookups). No alternatives are named, leaving usage somewhat vague.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations provide readOnlyHint=true, indicating safe read operation. The description adds valuable context beyond annotations by specifying the return data format (temperature, humidity, wind, conditions) and geographic scope. However, it doesn't mention rate limits, data freshness, or error conditions that would enhance behavioral understanding.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with two sentences that each earn their place: the first states purpose and scope, the second specifies return values. No wasted words, front-loaded with essential information, and appropriately sized for this tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, 100% schema coverage, and readOnlyHint annotation, the description provides good contextual completeness. It covers purpose, scope, and return format. The main gap is lack of output schema, but the description compensates by specifying return values. Could benefit from mentioning data source or accuracy limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with all parameters clearly documented in the schema. The description adds minimal value beyond the schema by mentioning 'city or coordinates' which aligns with the location/lat/lon parameters. No additional syntax, format, or constraint details are provided beyond what's in the structured schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 verb ('Get') and resource ('current weather'), specifying scope ('any city or coordinates worldwide') and distinguishing it from sibling tools like currency or translate. It explicitly mentions what data is returned, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by stating 'for any city or coordinates worldwide', but provides no explicit guidance on when to use this tool versus alternatives like ai_chat or ai_images. There's no mention of prerequisites, limitations, or comparative use cases with sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.