discoveries

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

With no annotations, the description carries the full burden. It discloses the return type ('list[dict]' or error dict) and adds a behavioral caution about not treating discoveries as proof. It implies read-only but doesn't explicitly confirm lack of side effects, though for a search tool this is acceptable.

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?

Four concise sections: purpose, usage, return, and a mistake to avoid. Each sentence contributes useful information without redundancy. Well-structured for an AI 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 simplicity (2 params, no nested objects) and the presence of an output schema, the description is complete. It covers when to use, what it returns, and a domain-specific caution. No significant 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?

Schema coverage is 0%, so the description must compensate. It provides some meaning for query ('focus the search on a failure, operator, or implementation technique') but does not explicitly explain domain. While the schema shows types and defaults, the description only partially adds value beyond the schema fields.

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 'Search reusable measured discoveries for a domain' with a specific verb and resource, and it distinguishes itself from sibling tools like open_run or submit_child by focusing on discovery search. The scope is clear.

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?

Explicitly states when to call ('before a mutation when the parent bundle needs broader evidence') and gives a contrasting mistake to avoid with submit_child, effectively providing when/why guidance. This goes beyond generic context.

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