get_experiment_score_averages

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

With no annotations, the description carries the burden and discloses key behaviors: client-side span walking, pagination up to max_spans, and returned aggregates. It does not mention performance costs or partial-result behavior when max_spans is reached, but the core behavioral profile 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?

Three concise sentences: purpose, usage condition, and return/pagination behavior. No filler or redundant restatement of the tool name.

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 read-only computation tool with no output schema, the description covers why to use it, what it returns, and its pagination limit. It could be more explicit about what happens if an experiment has more spans than max_spans, but overall it is sufficient.

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% and both parameters have descriptions. The description adds only marginal context for max_spans ('Pages through up to max_spans') that mostly restates the schema's 'Maximum spans to walk (default: 500).' 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?

Description opens with a specific verb and resource: 'Compute average score per evaluator for an experiment by walking the spans client-side.' It clearly distinguishes itself from backend summary endpoints and states the exact output (avg, min, max, count per evaluator).

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?

Explicitly states when to use: 'Use this when the backend summary/histogram endpoints return empty score aggregates (known issue on some experiments).' This gives a clear trigger condition and implicitly identifies the alternative approach.

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