agda_infer_in_goal

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

No annotations are provided, so the description carries the burden. It discloses that it works in a goal's local context with locally-bound variables, and contrasts with top-level inference. However, it doesn't mention prerequisites like needing an active agda_load or handling invalid goal IDs, limiting behavioral 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 concise and well-structured: a clear opening sentence, one comparative sentence for differentiation, and a compact Args list. No fluff.

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 typed expression tool with 3 fully documented parameters, an output schema, and clear differentiation among many siblings, the description is quite complete. It could mention preconditions (e.g., file loaded via agda_load), but the goal_id reference hints at 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?

Schema properties lack descriptions (0% coverage), but the description's Args section explains each parameter: file_path as absolute path, goal_id from agda_load output, and expr to type-check. This fully compensates for the schema gap.

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 states 'Infer the type of an expression in the context of a specific goal.' This is a specific verb+resource+context, and it explicitly contrasts with agda_infer, distinguishing it from a 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?

It explicitly says 'Unlike agda_infer which works at top level, this infers within a goal's local context (with access to locally-bound variables).' This names the alternative and clarifies the appropriate context.

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