recommend_crs

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions support for specific use cases and regional systems, but does not disclose critical traits such as whether this is a read-only operation, if it requires authentication, potential rate limits, or what the output format might be (e.g., a recommended CRS code or detailed analysis). For a recommendation tool with complex inputs, 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 appropriately sized with two sentences: the first states the core purpose and use cases, the second adds regional specificity. It is front-loaded with the main function, and each sentence adds value (e.g., listing supported purposes and Japan-specific zones). Minor room for improvement in structuring the use-case list more clearly, but overall efficient with zero 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 (3 parameters with nested objects, no output schema, and no annotations), the description is incomplete. It lacks information on output format (what the recommendation looks like), behavioral constraints (e.g., is it deterministic or heuristic-based), and how to interpret results relative to sibling tools. Without annotations or output schema, the description should compensate more to guide the agent effectively.

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 thoroughly. The description adds no specific parameter semantics beyond what's in the schema—it does not explain how 'purpose' influences recommendations, how 'location' details are prioritized, or how 'requirements' affect the output. Baseline 3 is appropriate as the schema does the heavy lifting, but the description fails to add meaningful context.

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: 'Recommend the optimal CRS based on purpose and location.' It specifies the verb ('Recommend') and resource ('optimal CRS'), and distinguishes from siblings by focusing on recommendation rather than comparison, listing, validation, or other operations. The mention of specific use cases (web mapping, distance calculation, etc.) and regional support (Japan Plane Rectangular CS) further clarifies scope.

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 through examples ('Supports web mapping, distance/area calculation...') and regional specificity ('Full support for Japan Plane Rectangular CS...'), suggesting it's for CRS selection tasks. However, it lacks explicit guidance on when to use this tool versus alternatives like 'compare_crs' or 'search_crs', and does not mention prerequisites or exclusions, leaving the agent to infer context from the input schema.

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