Latvia Statistics Table Metadata

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior, so the bar is lower. The description adds useful behavioral context: it reveals the exact return fields, explicitly frames the tool as a pre-query discovery step, and clarifies that no authentication is needed. It does not disclose potential error cases or rate limits, but for a simple metadata lookup this is acceptable.

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 compact and front-loaded: it leads with the core purpose, then gives a usage directive, a concrete example path with explanation, and a source/auth note. Every sentence earns its place; there is no fluff or repetition of the name/title.

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 single-parameter metadata lookup with an output schema already present, the description covers all essential context: what is returned, how to obtain a valid table_path, the ordering relative to table_query, an illustrative example, and the source plus auth requirement. Nothing an agent needs to call it 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 100%, with the table_path parameter fully documented in the schema itself, including the example path and its provenance ('type='t' from latvia-statistics.catalog'). The tool description repeats the example and adds the usage-before-table_query hint, but this adds marginal meaning beyond the schema. The baseline of 3 is appropriate.

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 uses a specific verb ('Get metadata') and identifies the exact resource ('a specific Latvia statistics table'), then enumerates the returned content: title, dimensions, valid value codes, and latest update timestamp. It also clearly distinguishes itself from the sibling table_query tool by positioning itself as the prerequisite discovery step, and from the catalog tool implicitly by taking a specific 'leaf table' path.

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 BEFORE latvia-statistics.table_query to discover accepted filter values', giving a clear when-to-use directive relative to its key sibling. It adds context about the data source and that no auth is required, but it does not explicitly state when not to use it or mention the catalog as the alternative for path discovery, so a full when-not list is absent.

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