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

The description supplies genuinely useful behavior beyond the annotations (keyword ranking rather than semantic, the returned fields, and the latest_data_date caveat), but it characterizes a pure read/search while the annotations declare readOnlyHint=false, idempotentHint=false and openWorldHint=false. That mismatch is a real inconsistency an agent could rely on incorrectly, so the transparency score is held down despite the otherwise rich detail.

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?

Front-loaded with purpose and scope, then ranking behavior, then alternatives, then a caveat 54 each sentence earns its place. The explicit list of returned fields is mildly redundant given an output schema exists, which keeps it from a 5.

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 discovery/search tool with an output schema and three simple parameters, the description covers purpose, ranking method, follow-up tools, and the latest_data_date interpretation caveat. Nothing an agent needs in order to call this correctly 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 description coverage is 0%, so the description carries the burden. It explains the query parameter ('by keyword') and hints at the schemas parameter ('across every schema'), but the limit parameter and the exact semantics/allowed values of schemas are never described, so compensation is only partial.

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 gives a specific verb and resource ('Find datasets by keyword across every schema') and positions the tool in the workflow ('the first discovery step'), which immediately separates it from siblings like describe_dataset and run_sql. An agent can tell what this tool returns without opening the schema.

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?

It explicitly routes the agent onward: use describe_dataset for detail, or run_sql on the series/dimensions tables for exact series, and frames this tool as the first discovery step. The when-to-use condition is stated rather than inferred.

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