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

Annotations already state destructiveHint=true and readOnlyHint=false, so the mutation behavior is declared. The description adds valuable context beyond annotations: the operation is 'deterministic fetch, no LLM cost,' requires AUTARIO_API_KEY, and returns row count and dataset_id. It also implies the table content is refreshed (overwritten), aligning with destructiveHint. No contradiction with annotations; the description enhances the safety/reliability picture.

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 a single paragraph of about four sentences, each serving a purpose: main action, scope, return value and follow-up, usage triggers, prerequisites and auth. It is front-loaded with the core function and avoids fluff. Slightly verbose in the middle, but efficient overall. It earns 4 because the information-to-length ratio is strong but could be tightened.

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?

This tool has only one required parameter, no output schema, and no nested objects, so complexity is low. The description covers everything an agent needs: what it does, when to use it, prerequisites, how to get the connector_id, what it returns (row count, dataset_id), how to consume the result (query_dataset / get_dataset_schema), auth, and determinism. No missing critical detail for correct 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 description coverage is 100% for the only parameter, connector_id. The description reinforces its meaning by stating where to get it ('from list_connectors') and adds the prerequisite that the connector must already exist (owner sets it up at autario.com/manage). This goes slightly beyond the schema by explaining the id's provenance and setup requirement, earning a 4 rather than the baseline 3.

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: 'Pull the latest numbers from one connected platform right now and refresh its hosted Postgres table on Autario.' This clearly distinguishes the tool from siblings like list_connectors (which lists connectors) and query_dataset/get_dataset_schema (which read datasets). It also generalizes across connector types (Search Console, Shopify, ads accounts, AI usage bills), making its scope unambiguous.

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 when to use it: 'Use when the user says "get the latest" or wants fresh data before analysis or a cross-platform weekly report.' It also provides a prerequisite and points to the alternative tool for getting the connector_id: 'get the connector_id from list_connectors.' The condition that the connector must already exist (set up in the UI) is a clear exclusion for when not to use it. This is comprehensive routing guidance.

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