Claude Agents Power

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'recommends' agents, implying a read-only operation, but does not clarify if it requires authentication, has rate limits, returns structured data, or handles errors. The description lacks details on output format or any behavioral traits beyond the basic action.

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, efficient sentence with no wasted words. It is front-loaded with the core purpose and appropriately sized for the tool's complexity. Every part of the description contributes directly to understanding the tool's function.

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 lack of annotations and output schema, the description is incomplete. It does not address what the recommendations look like (e.g., list of agents, scores, details), how many results are returned, or any error conditions. For a recommendation tool with no structured output information, the description should provide more context to guide usage effectively.

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 input schema has 100% description coverage, with the 'keywords' parameter fully documented in the schema. The description adds no additional parameter semantics beyond what the schema provides (e.g., it does not explain keyword formatting, examples beyond the schema, or how recommendations are generated). Thus, it meets the baseline score for high 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 the tool's purpose: 'Recommend agents based on project keywords.' It specifies the verb ('recommend') and resource ('agents'), and indicates the input trigger ('project keywords'). However, it does not explicitly differentiate from sibling tools like 'search-agents' or 'list-agents,' which prevents a perfect score.

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 no guidance on when to use this tool versus alternatives. It does not mention when to prefer it over siblings like 'search-agents' or 'list-agents,' nor does it specify prerequisites or exclusions. The usage context is implied but not articulated, leaving gaps for an AI agent.

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