team_expertise_map

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

No annotations are provided, so the description carries the full burden. It states the tool is read-only by describing it as returning data and clarifies data source: 'Built automatically from learn_from_attempts(author=...) calls — no setup needed.' This implies no side effects and no destructive behavior, which is sufficient for an overview tool.

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 sentences, each earning its place: purpose, output details, and usage/background. It is front-loaded with the key verb and resource. No 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?

The tool has no output schema, so the description must describe the output. It lists returned fields (lesson count, top domains, last-active date), which is helpful. It lacks details on ranking criteria and output format, but for a simple overview tool, this is largely sufficient. The description compensates well for the missing output schema.

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% (both parameters have descriptions). The tool description does not add any extra meaning beyond what the schema already provides. For example, it does not explain how `top_n` affects the output or how `instance_id` is used. Baseline 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's purpose: 'Full team expertise overview — who knows what, at a glance. Returns a ranked table of all contributors...' This is specific and distinguishes it from sibling tools like team_roster (listing members) and brain_who_knows (likely more specific lookups). The verb 'returns' and resource 'expertise overview' are precise.

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 explicitly lists use cases: 'Use for onboarding (who to ask about X?), retrospectives, or to find knowledge gaps.' This provides clear guidance on when to use the tool. It does not mention alternatives or when not to use it, but the scenarios are specific enough.

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