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 what the tool does without behavioral details. It lacks information on permissions, rate limits, response format, or whether this is a read-only operation, leaving significant gaps for a tool that likely queries external resources.

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 with zero waste. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration.

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 annotations, no output schema), the description is minimally adequate. It covers the basic purpose but lacks behavioral context and usage guidelines, making it incomplete for optimal agent understanding without additional structured data.

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 both parameters. The description adds no additional meaning beyond implying that 'feature' relates to PaymanAI integration and 'language' specifies code examples, which is already clear from the schema. 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 action ('Get') and resource ('code examples for PaymanAI integration') with specific scope ('Node.js or Python'). It distinguishes from siblings by focusing on code examples rather than documentation or problem-solving, though it doesn't explicitly contrast with each sibling tool.

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?

No guidance is provided on when to use this tool versus alternatives like 'get-documentation' or 'search-documentation'. The description implies usage for code examples but offers no explicit context, prerequisites, or exclusions for tool selection.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks critical details: it doesn't specify if this is a read-only operation, what format the documentation is returned in (e.g., text, HTML, markdown), whether there are rate limits, or if authentication is required. For a tool with no 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.

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

The description is a single, efficient sentence that front-loads the core purpose without any wasted words. It's appropriately sized for a simple tool with one parameter, making it easy to parse and understand 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 no annotations and no output schema, the description is incomplete for a documentation retrieval tool. It doesn't explain what is returned (e.g., content format, structure, or errors), lacks behavioral context like read-only nature or authentication needs, and doesn't differentiate from siblings. For a tool with 100% schema coverage but missing output and annotation context, it should provide more usage and behavioral details.

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 'topic' fully documented in the schema including an enum list. The description adds no additional parameter semantics beyond implying retrieval based on a topic, which is already covered by the schema. This meets the baseline of 3 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 action ('Get') and resource ('PaymanAI documentation on a specific topic'), making the purpose immediately understandable. It distinguishes from siblings like 'search-documentation' by specifying retrieval of documentation rather than searching, though it doesn't explicitly contrast with 'get-code-examples' or 'get-sdk-help' which might overlap in retrieving documentation-related content.

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. With siblings like 'search-documentation' and 'get-code-examples', it's unclear if this tool is for structured topic retrieval while others handle queries or examples. No exclusions or prerequisites are mentioned, leaving usage context ambiguous.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'gets help' but doesn't specify what kind of help (e.g., API references, troubleshooting, examples), how it's delivered (e.g., text response, links), or any constraints like rate limits or authentication needs. 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.

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 and appropriately sized, making it easy for an agent to parse quickly and understand the core function.

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 lack of annotations and output schema, the description is incomplete for a tool with 2 parameters. It doesn't explain what the help output entails (e.g., text explanations, code snippets), potential limitations, or how it differs from siblings, leaving the agent with insufficient context to use the tool effectively beyond basic parameter input.

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, clearly documenting both parameters ('sdk' with enum values and 'feature' as a string). The description mentions 'Node.js or Python SDK usage', which aligns with the 'sdk' parameter but doesn't add meaningful semantic context beyond what the schema provides, such as examples or usage tips for the 'feature' parameter.

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 as 'Get help with Node.js or Python SDK usage', which specifies the action (get help) and the resources (SDK usage for two languages). However, it doesn't explicitly differentiate from sibling tools like 'get-documentation' or 'solve-problem', which might offer similar assistance, so it doesn't reach the highest 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 like 'get-documentation' or 'solve-problem'. It implies usage for SDK help but lacks explicit context, exclusions, or comparisons to sibling tools, leaving the agent to infer usage scenarios.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action ('Search') without detailing traits such as search scope, result format, pagination, rate limits, or authentication needs. This is inadequate for a tool with zero annotation coverage.

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 with zero waste. It is front-loaded and appropriately sized for the tool's simple purpose, earning the highest score for conciseness.

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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the search returns, how results are structured, or any behavioral context. For a search tool with no structured support, this leaves significant gaps in 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?

The schema description coverage is 100%, with the 'query' parameter documented as 'Search term'. The description adds no additional meaning beyond this, such as query syntax or examples. Given the high schema coverage, the baseline score of 3 is appropriate.

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 ('Search') and resource ('PaymanAI documentation'), making the purpose evident. However, it doesn't differentiate from sibling tools like 'get-documentation' or 'solve-problem', which might have overlapping functionality, so it doesn't achieve the highest 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 like 'get-documentation' or 'solve-problem'. It lacks explicit instructions on context or exclusions, leaving the agent to infer usage 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.

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 'Get[s] help' but doesn't explain what form this help takes (e.g., troubleshooting steps, error resolution, documentation links), whether it requires authentication, rate limits, or any side effects. This leaves significant gaps in understanding how the tool behaves beyond its basic purpose.

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: 'Get help with common PaymanAI integration issues'. It is front-loaded with the core purpose and contains no unnecessary words or redundant information, making it highly concise 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 complexity (2 parameters, no output schema, no annotations), the description is incomplete. It lacks details on what the tool returns (e.g., help text, links, steps), behavioral aspects like error handling or prerequisites, and differentiation from siblings. Without annotations or output schema, the description should provide more context to fully inform usage.

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 clear descriptions for both parameters: 'problem' ('Describe the issue you're experiencing') and 'sdk' ('Which SDK you're using'). The description doesn't add any additional meaning beyond what the schema provides, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the schema adequately documents the parameters.

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: 'Get help with common PaymanAI integration issues'. It specifies the action ('Get help') and the resource ('common PaymanAI integration issues'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get-sdk-help' or 'search-documentation', which might offer similar assistance.

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 'common PaymanAI integration issues' but doesn't specify what qualifies as 'common' or when to choose this over sibling tools such as 'get-sdk-help' or 'search-documentation'. There are no explicit when/when-not statements or named alternatives.

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