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 does mention the comprehensive nature of the returned data ('performance metrics, supply chain information, detailed explanations'), which adds useful context about output richness. However, it doesn't address important behavioral aspects like error handling, rate limits, authentication requirements, or whether this is a read-only operation (though 'get' implies it likely is).

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 reasonably concise with two sentences that pack substantial information. The first sentence states the core functionality, while the second elaborates on the return data. There's no obvious fluff or redundancy. However, it could be slightly more front-loaded by mentioning the comprehensive return data earlier.

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 single parameter with full schema coverage and no output schema, the description provides adequate but incomplete context. It helpfully describes the richness of the return data ('comprehensive metric descriptions', 'performance metrics', 'supply chain information'), which compensates for the missing output schema. However, for a tool that returns complex data structures, more detail about the response format would be beneficial.

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 single 'domain' parameter clearly documented. The description doesn't add any parameter-specific information beyond what the schema already provides. It mentions 'domain name' but doesn't elaborate on format requirements, validation rules, or examples. The baseline score of 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: 'Get detailed publisher information by domain name' which is a specific verb+resource combination. It distinguishes itself from sibling tools like 'get_publisher_by_id' by specifying the domain-based lookup approach. However, it doesn't explicitly contrast with 'get_publisher_metadata' which might have overlapping functionality.

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. While it mentions domain-based lookup, it doesn't specify when to choose this over 'get_publisher_by_id' (ID-based lookup) or 'get_publisher_metadata' (which might provide different metadata). There's no mention of prerequisites, error conditions, or typical use cases.

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 describes the return content ('formatted data including performance metrics, supply chain information, and detailed explanations') but lacks critical behavioral details such as error handling (e.g., what happens if the ID is invalid), authentication requirements, rate limits, or data freshness. For a read operation with zero annotation coverage, 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.

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

The description is appropriately sized and front-loaded, starting with the core purpose. Both sentences earn their place: the first defines the action and scope, and the second elaborates on the return data. There's no unnecessary verbosity, making it efficient for an agent to parse.

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 (a single-parameter read operation) and the absence of annotations and output schema, the description is moderately complete. It covers the purpose and return content but misses behavioral aspects like error handling or prerequisites. Without an output schema, it should ideally detail the return structure more explicitly, but it provides enough context for basic use, albeit with gaps.

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 single parameter 'publisherId' documented as 'Publisher ID to search for.' The description adds minimal value beyond this, only implying the parameter is used to fetch data. Since the schema already does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't provide additional syntax, format, or usage details for the 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: 'Get detailed publisher information by Publisher ID with comprehensive metric descriptions.' It specifies the verb ('Get'), resource ('publisher information'), and key identifier ('Publisher ID'). However, it doesn't explicitly differentiate from sibling tools like get_publisher_by_domain or get_publisher_metadata, which likely retrieve similar data using different identifiers or scopes.

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 the tool retrieves data by Publisher ID but doesn't compare it to siblings like get_publisher_by_domain (which might use a domain instead) or get_publisher_metadata (which could offer different data types). Without such context, an agent might struggle to choose the correct tool for a given scenario.

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. It discloses that the tool returns detailed publisher information with metrics and explanations, which adds behavioral context. However, it lacks details on error handling, rate limits, authentication needs, or response format structure, leaving gaps for a tool with no 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 appropriately sized and front-loaded, starting with the core purpose and requirements. The bullet-point list efficiently organizes return information, but some sentences could be more concise (e.g., 'Each metric includes detailed explanations...' might be implied). Overall, it earns its place with minimal 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 no annotations and no output schema, the description provides good context on what the tool does and returns, including metric explanations. However, it lacks details on error cases, response format, or pagination behavior (implied by limit/offset but not explained), making it incomplete for full agent understanding without 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 already documents all parameters (publisherId, publisherDomain, limit, offset). The description adds value by specifying that either publisherId or publisherDomain is required, which clarifies semantics beyond the schema's optional parameters. However, it does not provide additional details on parameter interactions or usage examples.

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 a specific verb ('Get') and resource ('detailed publisher metadata from OpenSincera API'), including comprehensive field descriptions. It distinguishes from sibling tools (get_publisher_by_domain, get_publisher_by_id) by emphasizing comprehensive metadata rather than basic lookup, and explicitly mentions it accepts either publisherId or publisherDomain.

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 ('Requires either publisherId or publisherDomain') and implies usage for detailed metadata. However, it does not explicitly state when to use this vs. the sibling tools (e.g., get_publisher_by_domain/get_publisher_by_id), which might be for simpler lookups, nor does it mention exclusions or 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe what 'health status' means (e.g., returns success/failure, latency metrics, error details), whether it requires authentication, or what happens on failure. This leaves significant behavioral gaps for a monitoring tool.

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, focused sentence with no wasted words. It's front-loaded with the core purpose and efficiently communicates the essential information 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?

For a health check tool with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes 'health status' or what the return values might be (e.g., boolean status, detailed diagnostics, error messages). Given the lack of structured fields, the description should provide more operational context.

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 tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters, maintaining focus on the tool's purpose. Baseline for 0 parameters is 4.

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 ('Check') and target ('health status of the OpenSincera API connection'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools (which are all publisher-related), though this may not be necessary since health_check serves a distinct monitoring purpose.

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 for verifying API connectivity, but provides no explicit guidance on when to use it versus alternatives or prerequisites. It doesn't mention if this should be used before other operations or as a diagnostic tool, leaving usage context somewhat vague.

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