blockrun_exa

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

No annotations are provided, so the description must cover behavioral traits. It includes pricing and endpoint details (POST methods, categories) but omits error handling, rate limits, authentication specifics, and side effects. The mention of a skill for more details partially compensates.

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 well-structured with bullet points, front-loading the purpose. Every sentence provides useful information (endpoints, body shapes, pricing, categories). The reference to an external skill is a minor trade-off for brevity.

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?

The description covers endpoints, body shapes, pricing, and categories comprehensively for a tool with no output schema. It acknowledges the lack of response details by directing to an external skill. Behavioral gaps (no annotations) are partially addressed with cost info.

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%, but the description adds substantial value with example body shapes for each endpoint, category options, and pricing. This goes well beyond the schema's brief parameter descriptions.

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 'Neural web search via Exa — understands meaning, not just keywords. Great for research.' It lists four specific endpoints (search, answer, contents, find-similar) with distinct purposes, differentiating itself from sibling search tools like blockrun_search.

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 implies use for research ('Great for research.') and provides endpoint-specific details and categories. However, it does not explicitly state when to prefer this tool over siblings or when not to use it, missing explicit exclusion guidance.

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