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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the appended 'Read-only' is redundant. The description does add real value beyond the annotations by disclosing the return shape (paginated summary with oppositionCount and nextHearingDate), which an agent cannot infer from the schema.

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, front-loaded with the dataset scope, then the routing hint, then a concrete example. Every sentence earns its place and nothing is padded.

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 nine-parameter filter tool with no output schema, the description covers the dataset scope, the return summary fields, the pagination behavior, and the hand-off to get_project for detail. Nothing material to correct invocation is missing.

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%, so the baseline is 3, and the schema itself well documents the parameters including the non-obvious default status bucket. The description adds meaning beyond the schema by showing how a fuzzy request ('solar projects in Texas waiting 3+ years') maps onto state, fuelType, and minYearsWaiting.

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 (Search) plus the exact resource (the WaitingForPower dataset of U.S. energy projects awaiting permitting), and enumerates the covered project types. It also differentiates from the sibling get_project by scope (summary list vs full detail).

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 says when to use this tool versus the alternative: this returns a paginated summary, while get_project with a slug returns full detail (sources, status history, hearings, opposition records). It even supplies a worked natural-language-to-parameter mapping example.

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