get_experiment_results

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

Annotations only declare readOnly and idempotent; the description adds genuinely new behavior: the exact result payload, that per-sample scores are large and displayed truncated for very large sets, and that failed/cancelled runs may return a status stub with a log tail. That is real disclosure an agent cannot get from the annotations.

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 sentences, zero filler, with the payload contents front-loaded, the optional-parameter cost second, and the edge case last. Every clause earns its place.

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?

No output schema exists, yet the description lists the returned fields and covers the degraded-response case, which is exactly the compensation required. An agent can call this correctly and interpret the response without further documentation.

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%, so the description must carry parameter meaning. It explains include_per_sample_scores (default off, adds raw scores, large payload, truncated display) and its trade-off, but says nothing about experiment_id's provenance or format, leaving half the parameters implicit.

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?

States a specific verb and resource ('Get the detection results for a completed experiment') and enumerates exactly what comes back: summary counts, average poisoning score, score distribution, threshold, important features, runtime. The word 'completed' separates it from get_experiment_status and get_experiment_logs without ambiguity.

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 scope 'for a completed experiment' gives clear context for when this tool applies, and the note about failed/cancelled runs returning a status stub prepares the agent for that case. It stops short of naming alternatives (e.g., use get_experiment_status while running), so no explicit exclusions are offered.

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