run_evaluation

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

The description discloses streaming queries and LLM-as-judge scoring, but does not mention side effects like writing results to output_dir, API costs, time consumption, or potential for partial results. Without annotations, these gaps reduce transparency.

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?

A single sentence that is front-loaded with the core action. It efficiently lists the main steps and outputs. While it could be split for readability, it contains no fluff and earns its length.

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 outputs (scores, summary, pass/fail) but omits operational context: it doesn't explain that results are saved to output_dir, the expected dataset format, or error handling. For a tool with 5 params, nested objects, and no output schema, these gaps are notable.

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%, so the schema already documents all 5 parameters. The description adds process context ('load a dataset, query models via streaming') but no significant parameter-level detail beyond what the schema provides. 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 it runs an LLM evaluation, lists the steps (load dataset, query models, score responses), and specifies outputs (per-question scores, summary, pass/fail). This differentiates it from sibling tools like evaluate_retrieval or check_retrieval_drift that focus on specific aspects.

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 vs alternatives (e.g., when to use evaluate_rag_end_to_end or check_thresholds). The description does not provide context, prerequisites, or exclusions, leaving the agent to infer usage without clear boundaries.

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