get_dataset_schema

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

With no annotations, the description carries the full disclosure burden and does so richly: it decodes the source_health enum (including the subtle inaccessible vs unknown distinction), clarifies that source_health_detail is a fixed reason code rather than provider text, distinguishes last_checked_at from last_refreshed_at, defines the advisory 'overdue' freshness rule, and confirms raw provider URLs and credentials are excluded.

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 opening sentence front-loads the purpose, and each subsequent sentence carries real semantic content about return fields rather than filler. It is dense and slightly long for a one-parameter read tool, but no sentence is redundant.

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 no output schema, no annotations, and a minimal input schema, the description supplies everything an agent needs: what the call returns, how to interpret the trust/freshness fields, and when in the workflow to invoke it. Nothing material 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?

There is a single parameter with 0% schema description coverage, so the description must compensate. It does, by annotating dataset_id with a concrete source ("e.g. from search_datasets"), which is exactly the guidance the bare string schema lacks.

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 and resource ("Get a dataset's schema") and immediately enumerates the returned surface (columns, geometry type, CRS/SRID, feature count, extent, trust metadata), which makes it unmistakable what this tool does versus search_datasets, get_features, or 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?

Explicitly says "Call this before writing spatial questions so you know both the available columns and whether the source may be stale," giving a clear triggering condition. It stops short of naming a specific alternative tool or a when-not-to-use case, so it does not reach the 5 bar.

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