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

Annotations already mark the operation read-only, idempotent, and non-destructive. The description adds meaningful behavior beyond that: no auth required, client-side filtering, no full-text search endpoint upstream, and the JSON-stat REST API source. This is good context without contradicting the 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 three tightly-packed sentences with no filler. It front-loads the core action and catalog scope, then adds filtering behavior, downstream usage, and data-source context. Every sentence earns its place.

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?

Given the output schema exists and annotations cover the safety profile, the description is complete for an agent to select and call this tool correctly. It names the data source, scope, filtering limitation, auth requirement, and how results feed into sibling tools.

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 the schema already documents query, limit, and offset with defaults and examples. The description adds the client-side filtering limitation and domain-level purpose, but it does not need to add much more because the schema carries the parameter semantics.

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 names a specific verb/resource combo: 'Search/browse the 675 statistical tables published by the Statistical Office of the Slovak Republic'. It also states the output purpose ('Returns cube_code + dimension_codes') and names its downstream siblings, making it clearly distinct from dataset_metadata and dataset_data.

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 clearly explains when this tool should be used: to find tables and obtain cube_code + dimension_codes for subsequent metadata/data calls. It does not explicitly list excluded use cases or compare against unrelated search tools, but the downstream routing is enough for an agent to choose it appropriately.

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