call_endpoint

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds critical behavioral details: duration classes, bulk billing, structured error responses, and retry success probability. There is no contradiction with 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?

The description is a single paragraph that efficiently packs purpose, usage guidelines, and behavioral notes. It is front-loaded with the primary action. Slightly long but no redundant sentences.

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 presence of an output schema and rich annotations, the description covers error handling, retry behavior, billing, and relationship to describe_endpoint. It is fully sufficient for correct agent usage.

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 description adds meaning by relating body and params to describe_endpoint's schemas, and by explaining that params are operation-specific. However, it does not describe limit, fields, or include_raw beyond the schema, so it partially compensates for the 33% schema coverage.

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 clearly states it calls a Sugra API endpoint by operation_id. This distinguishes it from sibling tools like describe_endpoint, which is for metadata, and fetch_data which likely has different semantics.

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 provides explicit guidance on when to use describe_endpoint first for agent_hints and parameters, advice on limiting parallelism and batching, and a retry strategy after upstream_timeout errors. This helps the agent decide when and how to invoke the tool.

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