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

Annotations already mark it read-only and idempotent; the description adds that no auth is required, gives the data source, and reveals that the response contains a dataflow_id for follow-up calls. This is helpful contextual behavior beyond 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?

Two sentences, with the key action and context packed efficiently. No filler. The mention of the follow-up tools is concise.

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 simple single-parameter interface and the presence of an output schema, the description covers everything an agent needs: what it returns (dataflow_id), how to use it with sibling tools, the source, and auth requirements. The ~204 count sets expectations.

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 coverage is 100%, so the query parameter is fully described in the schema. The description's mention of filtering by name/id and optionality mirrors the schema without adding new semantic detail. 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?

States a specific action ('Search or list') on a specific resource ('INE Chile SIMEL dataflows') with rich context about what these dataflows are and how they are used. Clearly distinguishes from siblings by describing the follow-up tools that consume the returned dataflow_id.

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 instructs when to filter vs list, and tells the agent to use the returned dataflow_id with ine-chile.structure and ine-chile.data, effectively routing to sibling tools. No ambiguity about when this tool is the right choice.

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