record_bench_result

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 key side effect: testing a hypothesis automatically updates its status. It also states the return value. Though it doesn't discuss permissions or failure modes, the main behavioral trait is well covered.

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 well-structured with sections for summary, context, args, and returns. Every sentence adds value, and the length is appropriate for the 7-parameter tool. It front-loads the purpose clearly.

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 description covers the operation, side effects, all parameters, and the return value. Given the tool's complexity (7 params, 3 required) and lack of annotations, this is a complete description that would allow correct invocation.

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 detailed explanations for all 7 parameters, including examples and default values (e.g., corner, bench_type, confirms). This fully compensates for the sparse schema.

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 records a simulation or measurement result and optionally links it to a hypothesis. This distinguishes it from sibling tools like record_hypothesis and record_decision by specifying the resource ('bench result') and the linking behavior.

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?

It provides clear context by mentioning the hypothesis→bench→feedback loop, implying when to use it. However, it lacks explicit 'when not to use' or named alternatives, so it falls short of a 5.

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