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

The annotations already indicate this is a read-only, non-destructive operation with open-world data. The description adds valuable behavioral context beyond annotations by disclosing the cost ($0.01/call) and specifying what information is returned (senders, subjects, dates), though it doesn't mention pagination or rate limits.

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 three focused sentences that each serve a distinct purpose: stating the tool's function, describing the return value, explaining the use case, and disclosing cost. There's no wasted verbiage and information is front-loaded appropriately.

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?

For a simple read operation with comprehensive annotations and full schema coverage, the description provides good contextual completeness. It explains what the tool does, what it returns, when to use it, and includes cost information. The main gap is the lack of output schema, but the description compensates by specifying the return format.

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?

With 100% schema description coverage, the input schema already fully documents the single parameter (limit with default value). The description doesn't add any parameter-specific information beyond what's in the schema, meeting the baseline expectation when schema coverage is complete.

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 ('Check the inbox'), resource ('ATXP agent email address'), and scope ('Returns a list of recent emails with senders, subjects, and dates'). It distinguishes itself from sibling tools like atxp_email_send by focusing on reading rather than sending emails.

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 ('Useful for AI agents that need to receive and process emails'), but it doesn't explicitly state when not to use it or mention alternatives. It doesn't compare with other email-related tools beyond the obvious distinction from atxp_email_send.

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 description adds valuable behavioral context beyond what annotations provide: it discloses the cost ('Cost: $0.01/call'), which isn't captured in the annotations. The annotations already indicate this is a non-readonly, non-destructive, non-idempotent, open-world operation, and the description doesn't contradict these hints. It provides useful additional information about financial implications.

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 defines the core functionality, and the second provides usage context and cost information. It's front-loaded with the essential purpose and wastes no 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?

For a tool with no output schema, the description provides good context about what the tool does and its cost implications. However, it doesn't describe what happens after sending (e.g., success confirmation, error handling, or delivery status), which would be helpful given the absence of output schema. The annotations cover safety aspects well.

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?

With 100% schema description coverage, the input schema already fully documents all three parameters (to, subject, body). The description doesn't add any parameter-specific semantics beyond what's in the schema, so it meets the baseline expectation without providing extra value.

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 ('Send an email'), identifies the resource ('from your ATXP agent email address to any recipient'), and distinguishes it from sibling tools like 'atxp_email_inbox' (which presumably receives emails). It provides a complete verb+resource+scope statement.

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 about when to use this tool ('Useful for sending automated notifications, reports, or messages on behalf of an AI agent'), but doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools. It offers practical guidance without exclusions.

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 cover readOnlyHint=false, destructiveHint=false, idempotentHint=false, and openWorldHint=true. The description adds valuable context beyond this: it discloses cost details ($0.05–0.15/call depending on size) and return format (URL or base64 data), which are not covered by annotations. No contradiction with annotations.

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 front-loaded with the core purpose, followed by return format, usage examples, and cost details in three concise sentences. Every sentence adds value without waste, making it efficient and well-structured.

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 (2 parameters, no output schema), the description is mostly complete: it covers purpose, return format, usage examples, and cost. However, it lacks details on error handling, rate limits, or authentication needs, which could be useful for an AI agent. Annotations provide some behavioral context, but more completeness could be achieved.

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 both parameters well-documented in the schema. The description adds minimal extra meaning by mentioning 'creative descriptions, photorealistic renders, artistic styles' as examples for the prompt, but this is redundant with the schema's guidance. Baseline 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.

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

The description clearly states the verb 'Generate' and the resource 'AI image from a text prompt', specifying the service provider 'ATXP's image generation service'. It distinguishes from sibling tools like atxp_music_generate by focusing on images rather than music or other functions.

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 usage by mentioning 'creative descriptions, photorealistic renders, artistic styles, and more', but it does not explicitly state when to use this tool versus alternatives like atxp_music_generate or atxp_web_search, nor does it provide exclusions or prerequisites.

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 description adds valuable behavioral context beyond annotations: it discloses the cost ('$0.15/call'), which isn't covered by the annotations (which only indicate it's not read-only, destructive, or idempotent, and is open-world). This financial implication is crucial for an agent to know. No contradictions with annotations exist.

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 efficiently structured in three sentences: purpose, output, usage guidance, and cost. Each sentence adds distinct value without redundancy. It's front-loaded with the core functionality and appropriately sized for the 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 (2 parameters, no output schema), the description is largely complete: it covers purpose, output format, usage tips, and cost. However, without an output schema, it could benefit from more detail on the return value (e.g., format of the URL, error handling). The annotations provide safety context, but the description compensates well overall.

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?

With 100% schema description coverage, the schema already fully documents both parameters (prompt and lyrics). The description adds some semantic guidance ('Describe genre, mood, tempo, instruments, or specific style for best results') that enriches the prompt parameter's meaning, but doesn't provide additional syntax or format details 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.

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 music track'), resource ('from a text prompt using ATXP's music generation service'), and distinguishes it from sibling tools (music generation vs. email, image, or web search). It provides concrete details about the output ('Returns a URL to the generated audio file').

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 ('Describe genre, mood, tempo, instruments, or specific style for best results'), but doesn't explicitly state when not to use it or name specific alternatives among the sibling tools. It implies usage for music generation needs but lacks explicit exclusions.

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 cover read-only, non-destructive, non-idempotent, and open-world hints, but the description adds valuable context beyond this: it specifies that results are 'clean, structured' with details like titles, URLs, and snippets, and mentions it's 'AI-powered' and 'great for' specific use cases. This enhances understanding without contradicting annotations.

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 front-loaded with the core purpose and key features, followed by use cases, all in two efficient sentences. Every sentence adds value without redundancy, making it appropriately sized and well-structured.

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, rich annotations, and 100% schema coverage, the description is largely complete. However, without an output schema, it could benefit from more detail on return values (e.g., result format or pagination), though it does mention 'structured results with titles, URLs, and snippets' which helps.

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 the 'query' and 'limit' parameters. The description does not add any specific meaning or details beyond what the schema provides (e.g., it doesn't explain query formatting or result structure further), 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 ('Search the web') and resource ('ATXP's AI-powered search engine'), distinguishing it from sibling tools like email or content generation. It provides concrete examples of use cases (researching topics, finding current information, etc.), making the purpose explicit and differentiated.

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 (e.g., researching topics, finding current information, checking facts, discovering resources), but it does not explicitly state when not to use it or name alternatives among sibling tools. This gives good guidance but lacks explicit exclusions or comparisons.

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