kopern_run_autoresearch

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

Discloses key behaviors: iterative mutation, grading, improvement retention, API key usage, and potential duration. Annotations (openWorldHint=true) align; description adds context beyond structured fields.

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?

Three concise sentences with front-loaded purpose. No redundant information; every sentence adds value. Efficient for a tool with multiple parameters.

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?

Covers optimization process, return value (optimized score), and important behavioral notes (API keys, duration). Lacks details on state changes (e.g., whether agent is replaced) but sufficient given no 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 covers all parameters with descriptions (100% coverage). Description provides general context but does not significantly add meaning beyond schema definitions. Baseline score applies.

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?

Description clearly states the tool's purpose: 'Run AutoTune optimization on an agent' with specific verbs and resource. It distinguishes from siblings like kopern_run_grading by highlighting iterative mutation and improvement.

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?

Implies usage for optimizing agent prompts but lacks explicit when-not-to-use guidance or comparison to similar tools like kopern_run_grading. Mentions time and API keys but no decision criteria.

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