generate_cone_search_code

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It does state that the tool 'generates Python code' rather than executing the search, which is useful. However, it does not clarify any requirements, potential failures, rate limits, or that the code must be run separately. This is adequate but not detailed.

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 well-structured with a clear opening, an explanation, and a dedicated 'Use Case' section. It is not overly long, though the first sentence is somewhat repetitive with the tool name. Overall, it is efficient and easy to scan.

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 has only two parameters, one required, and rich schema descriptions, the contextual information is sufficient. The use case adds valuable guidance, and the presence of an output schema reduces the need to explain return values. The description could mention the format of the generated code, but this is not a major gap.

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 detailed parameter descriptions and examples for both coordinates and radius. The description adds little beyond the schema, merely restating that the search is 'within a radius of specified sky coordinates.' Given the high schema coverage, a baseline 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 opens with a specific verb and resource: 'Generate code to perform ZTF cone searches at multiple positions.' It clearly differentiates from sibling tools like search_sources_near_position by emphasizing code generation rather than execution. The mention of 'multiple positions' also adds scope specificity.

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 'Use Case' section explicitly states when to use the tool: when you have a list of coordinates and want to find ZTF detections nearby. It provides practical context such as catalog cross-matching and coverage checks. However, it does not explicitly mention alternatives or when not to use this tool.

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