brain_who_knows

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

No annotations are provided, so the description carries full burden. It describes the data source (learn_from_attempts), the ranking criteria (lesson count and confidence), and provides an example output. It does not mention side effects or auth requirements, but the tool appears to be a read-only query, and the description is sufficiently transparent about its behavior.

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 three short sentences and an example. It is front-loaded with the purpose, followed by source, output detail, usage context, and a concrete example. Every sentence adds value with no redundancy or wasted words.

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 3 parameters (all explained), no output schema but an example output, and moderate complexity (expert ranking from knowledge graph), the description covers the input, behavior, and usage context well. It lacks error handling details, but the example and clear purpose make it largely complete.

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 each parameter having a clear description. The description adds an example of usage (brain_who_knows(topic="kubernetes deployment") → ...) and implies the default for limit is 10. This adds some context but does not significantly expand on the schema descriptions, so 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 clearly states the tool finds experts on a given topic using the knowledge graph from learn_from_attempts, and returns a ranked list ordered by lesson count and confidence. It includes an example output, distinguishing it from other brain_* tools that provide different analyses (e.g., brain_conflicts, brain_coverage).

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 to use this tool to find the right person before starting a task or to understand knowledge distribution. While it does not list when not to use it or alternatives, the sibling tools do not offer a direct replacement for topic-specific expert finding, making the guidance clear enough.

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