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

Annotations already declare readOnlyHint=true and idempotentHint=true, indicating a safe read operation. The description adds behavioral context with 'Aggregate counts only; not legal advice,' which is a valuable caution about limitations. No contradiction with 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, each earning its place: purpose, usage scenario, and alternative+disclaimer. Front-loaded with the key action. No redundant or verbose language.

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 statistical tool with a separate output schema (exists but not shown), the description sufficiently covers purpose, usage, parameter hints, and a disclaimer. It does not need to explain the output format since that is in the schema. Overall, it is complete for an AI agent to decide and invoke correctly.

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 compensate. It does so through the example ('we sampled 400 culled docs and found 2 relevant') which implies usage of relevant_found_in_sample and sample_size, but does not explicitly describe each parameter, especially confidence_level. This is adequate but not thorough.

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's purpose: estimating responsive material remaining in an unreviewed set. It specifies the verb 'estimate', the resource 'elusion rate and confidence interval', and distinguishes from sibling 'calculate_tar_recall_estimate' by explicitly naming it as an alternative.

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?

Provides explicit when-to-use guidance: 'Use when a random sample of that excluded set has been coded' with an illustrative example. It also tells the agent when not to use it: 'For an overall recall % from the same sample, use calculate_tar_recall_estimate.' This is exemplary guidance.

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