get_trial_results

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

The description discloses the tool's behavior for four distinct cases and the default adverse event filtering, which adds value beyond the schema. However, it lacks details on error handling or other behavioral traits, though no annotations exist to carry that burden.

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 sentences that front-load the main purpose and efficiently cover key details like the four cases and default AE grade.

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?

For a tool with no output schema, the description adequately explains what the tool returns (components of results and cases), making it complete for an agent to understand the tool's function.

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 description adds minimal extra meaning beyond the schema. It restates the default for ae_grade but does not significantly enhance understanding of 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 fetches posted results for a clinical trial, listing specific components like participant flow, baseline characteristics, and outcomes, distinguishing it from siblings like get_trial_details.

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 indirectly implies usage by listing the four cases it handles, but it does not explicitly state when to use this tool versus alternatives like get_trial_details or compare_trials.

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