Pickrate

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

No annotations provided; description doesn't disclose pagination, error handling, data freshness, or read-only nature. It only states what it returns, not how it behaves operationally.

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?

Two sentences, front-loaded with main purpose, no redundant words. Efficient and clear.

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?

Covers primary functionality and parameter usage, but lacks details on return format, error conditions, or behavior boundaries. Adequate for a simple read tool but could be more informative.

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 covers both parameters fully. Description adds value by explaining the interaction between category and domain: omitting category lists categories. This clarifies the optionality beyond the 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?

Clearly states it retrieves a ranked list of tools by pick rate for a category, or lists categories when category is omitted. Verb 'Get' with specific resource. Distinguishes from siblings getPickRate and lookupTool.

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?

Describes when to omit category to list categories and optionally scope by domain. Implicitly differentiates from siblings by describing its own purpose, but lacks explicit when-not-to-use or direct alternative references.

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 the full burden. It states the returned data (Pick Rate and rank) but does not disclose error handling, authentication needs, or idempotency. It adds limited behavioral context beyond the obvious.

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, well-structured sentence with no extraneous words. Every part adds value.

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 one-parameter tool without output schema, the description adequately explains the output (headline Pick Rate and rank). It could mention return format or edge cases, but is generally complete.

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 coverage is 100% with a clear description for the 'tool' parameter. The description repeats this type information without adding new semantics, so baseline 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 tool retrieves the headline Pick Rate and rank for a single developer tool, and explains what Pick Rate means. This verb+resource phrasing distinguishes it from siblings like getLeaderboard (likely a ranking list) and lookupTool (generic lookup).

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 does not explicitly state when to use this tool versus alternatives. It implies usage when needing Pick Rate for a specific tool, but no guidance on when not to use it or how it differs from getLeaderboard or lookupTool.

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 output includes 'Pick Rate, rank, who beats it', which is helpful but does not mention potential side effects, error handling, rate limits, or authorization requirements. The transparency is adequate for a simple lookup but lacks completeness.

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, well-structured sentence that front-loads the core action and resource. No extraneous words or filler; every part earns its place.

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 low complexity (single parameter, no nested objects, no output schema) and the presence of sibling tools for context, the description is sufficient. It specifies exactly what input is accepted and what the output contains. A minor gap is the lack of explanation about how to interpret the output, but the description provides enough for an agent to decide when to invoke it.

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% coverage with a description for the single parameter 'query' that already lists acceptable types (name, slug, domain, package). The tool description redundantly states these types, adding no new semantic value beyond what the schema provides. Baseline 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 uses the specific verb 'Resolve' and identifies the precise resource 'Pickrate report' including its contents (Pick Rate, rank, who beats it). This clearly distinguishes it from sibling tools 'getLeaderboard' and 'getPickRate', which likely serve different purposes (e.g., listing or single metric).

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 when to use this tool: when you have a tool identifier (name, slug, domain, or package) and want its full report. While it doesn't explicitly state 'do not use for leaderboards or general metrics', the context signals from sibling tool names make the intended use clear.

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