get_agent_sample

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

With no annotations, the description carries the full burden. It discloses that the tool selects N random agents from a specific breed (or all turtles when breed is None) and returns requested attributes as a markdown table. It does not mention side effects or error conditions, but the read-only nature is implied. A slight gap in explicit non-destructive guarantee prevents a 5.

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 with two paragraphs. The first sentence states core purpose, and the second paragraph adds context and detail. Every sentence earns its place, and the structure front-loads key information.

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 100% schema coverage, no required parameters, and an output schema, the description is complete. It explains the tool's role relative to siblings and describes the selection logic and default attributes. No missing critical context.

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%, so baseline is 3. The description does not add significant meaning beyond what the input schema already provides for n, breed, and attributes. It restates defaults but does not introduce new parameter semantics.

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 returns a sample of agents with selected variables as a markdown table. It explicitly distinguishes from siblings by mentioning 'aggregates only' for get_world_state and 'hand-crafted report calls' for report, giving specific purpose context.

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 provides explicit guidance on when to use this tool: as a middle ground between get_world_state (aggregates) and report (custom calls). It implies when not to use by contrasting with these alternatives, making usage clear.

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