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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, and the description does not contradict them. Beyond the annotations it adds concrete behavioral context: the returned entries carry live dataset_id and datasets[] with per-report materialization, refresh interval/last refresh time, the fact that connector creation happens in the Autario UI, and the API key requirement. These details are meaningful for a no-parameter tool.

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 several sentences long but every sentence earns its place: purpose, use cases, return structure, and operational context. The pipe-delimited formatting packs information compactly. It is slightly dense and could be trimmed, but it is well front-loaded with the core purpose and stays on-topic.

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 zero parameters and no output schema, this description is exceptionally complete. It explains what each returned entry contains, how to use the dataset_id with query_dataset, when to call it, who creates connectors, and the credential/API-key boundaries. There are no missing operational details an agent would need to invoke this tool correctly.

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?

The tool has zero parameters, so the schema coverage is 100% and there is nothing for the description to add about parameter semantics. Per the rubric baseline, a 4 is appropriate when no parameters exist; the description instead usefully explains what the response conveys (dataset_id, datasets[], refresh metadata), which belongs more to completeness than 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 opens with a specific verb and resource: 'List the platform connectors set up on this Autario account', then enumerates the connector categories (search, web, ads, etc.). It also distinguishes this tool from refresh_connector and names the alternative for querying the resulting dataset_id. This fully separates it from siblings without needing 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?

The description gives explicit trigger phrases ('my Search Console data', 'my ad spend', 'my store orders', cross-platform weekly report) and states when to prefer it: 'call this instead of guessing which ones exist.' It also clarifies what this tool does not do ('never handles credentials') and points to refresh_connector for refresh operations, providing both when-to-use and an implicit when-not.

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