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

Beyond the readOnly/idempotent annotations, the description discloses ranking logic (topic-relevance, source quality tiers, coverage, recency), quality gates (is_public, completeness, scope, length), and output characteristics (tagline + sample facts). It also reveals token-efficiency behavior and how to interact to minimize cost. 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?

The description is longer than average but every sentence carries useful information: purpose, usage context, ranking logic, output value, and a token-saving tip. It is front-loaded with the core purpose and contains no filler. Slightly overlong due to the detailed tier breakdown, but justified.

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 no output schema, the description adequately explains return values (quality-ranked list with tagline and sample facts). It covers selection criteria, quality filters, source scope, and usage guidance. Combined with rich annotations (readOnly, idempotent) and fully documented parameters, nothing critical is missing for safe and effective 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 coverage is 100% and each parameter already has a meaningful description, so baseline 3 applies. The description adds context about ranking criteria and quality gates but does not materially enrich the meaning of the individual parameters beyond what the schema provides.

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 verb and resource: 'Discover the most relevant verified datasets for a given topic,' with concrete catalog sources. It distinguishes itself from query_dataset by noting the results include taglines and sample facts so you can pick datasets without additional round-trips, and it clearly identifies the tool as a quality-ranked discovery search rather than a generic dataset query.

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?

The description explicitly says 'Use this when starting an article, dashboard, or analysis on a topic,' giving a clear trigger context. It also references an alternative by explaining you can avoid further query_dataset round-trips. However, it does not explicitly contrast with search_datasets or other sibling discovery tools, so the when-not guidance is slightly incomplete.

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