brain_find_tool

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

With no annotations provided, the description carries the behavioral burden. It discloses that the tool returns definitions rather than executing tools, indicates relevance ranking, and references the list_tools output format. It does not explicitly state lack of side effects, but the search/return framing makes the read-only behavior clear enough.

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 short sentences with no filler. The action is front-loaded and the return behavior is described in the second sentence. Every word 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?

For a one-parameter, output-schema-bearing search tool, the description is complete: it states the query source, the result type, ranking behavior, and output format. No critical invocation information is missing.

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% and the single 'query' parameter is already described as a natural language query. The description adds no additional semantic detail beyond the schema, so the 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 clearly states a specific action ('Search for tools') and resource ('tool definitions'), and adds that results are ranked by relevance. This differentiates it from session lifecycle tools and brain_call_tool without ambiguity.

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 its use case: natural-language discovery of tools, as opposed to session manipulation or direct tool invocation. However, it does not explicitly name alternatives or state when not to use it, such as preferring list_tools for browsing all tools.

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