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

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint), the description discloses important behavioral caveats: daily snapshot, observation window ≈ 2 weeks retention, and n being nominal due to correlated trials. This adds meaningful context about data freshness and statistical limitations.

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?

Despite some length, the description is highly structured with headers (Purpose, Triggers, When to call, Prerequisites, Next steps, Caveats, Args, Disclaimer), and the essential purpose is front-loaded. Every section serves a distinct functional purpose, from trigger matching to parameter disambiguation.

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?

With an output schema present, return values need no explanation. The description covers purpose, triggers, usage context, prerequisites, next steps, data caveats, and all parameter semantics, making it fully self-sufficient for an agent to decide when and how to invoke this tool.

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%, but the description's Args section fully explains each parameter: market_id with enum-like examples, interval with example values, and variant with detailed semantics ('v1' vs 'v2', default, accumulating since date). This fully compensates for the schema's lack of descriptions.

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 opens with a specific verb and resource: 'Reliability diagram data for Level-1 signal confidence — realized hit rate per confidence bucket ... with ECE summary.' It clearly distinguishes itself from siblings like get_structure_calibration and get_prediction_accuracy by focusing on signal confidence calibration.

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?

Explicit 'When to call' guidance is provided: 'before trusting get_signals confidence values as probabilities.' Trigger phrases are given, and 'Next steps' names alternative tools (get_prediction_accuracy, get_signals), offering clear usage direction and context.

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