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

With readOnlyHint=false and destructiveHint=false already signaling a safe write, the description adds useful behavioral context: the request is logged and read by the people who build the dataset, meaning it is a human-reviewed feedback channel rather than an immediate data fetcher. It does not explain whether the agent receives any confirmation beyond the output schema, but that is largely covered by the existing output schema. 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?

Two short sentences with no wasted words. The core request is front-loaded, followed by the examples and the logging behavior, making it easy to scan and act on.

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?

The tool is low-complexity with two fully documented parameters, an output schema, and annotations covering safety. The description supplies the missing conceptual context: this is a human-reviewed request log for missing data, not a retrieval operation. It could be slightly more complete by explicitly directing the agent to first try existing search tools, but overall it is sufficient for correct invocation.

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 100%, so both the purpose and description parameters are already fully documented in the schema, establishing a baseline of 3. The description adds examples of the kinds of data to mention (other courts, states, case types, fields, documents), but it does not clarify how to distinguish the purpose parameter from the description parameter. This is baseline-level value over the schema.

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 states a specific action: tell the dataset maintainers about missing data you need, with concrete examples (other courts, states, case types, fields, documents). This distinguishes it from retrieval siblings like search_cases and get_case, though it does not explicitly name an alternative. The verb-and-resource combination is clear enough for an agent to select it.

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?

Usage is implied by 'data this dataset does not have but you need,' which tells the agent to use this when existing search/get tools cannot satisfy the need. There are no explicit when-not conditions or named alternatives (e.g., 'use search_cases first'), leaving the agent to infer the boundary. This is adequate but not strong guidance.

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