run_eval

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

No annotations are provided, so the description carries the burden. It discloses key behaviors: simulation via the LLM, no audio cost, scoring dimensions, and output format. It doesn't mention side effects or parameter-driven choices, but the main execution model is clear.

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 single sentence is information-dense and front-loaded with the main action. Every clause adds value: simulation, cost, scoring dimensions, and report style.

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 explains high-level behavior but omits parameter semantics and usage context. With no output schema and no annotations, the agent will struggle to know valid inputs or what N represents.

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 0%, and the description mentions none of the three parameters (assistantId, market, vertical). It doesn't explain what market or vertical mean for the evaluation, leaving the agent to guess.

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 function: it tests the agent via simulated scenarios, scores behavior on specific criteria, and returns a /100 report. This distinguishes it from sibling tools like list_agents or deploy_agent.

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?

No guidance on when to use this tool instead of alternatives like regression_gate or audio_forensics. The description implies evaluation use but doesn't state prerequisites or exclusions.

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