get_agent

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool returns specific data fields, but does not cover critical aspects such as error handling (e.g., what happens if the symbol is invalid), rate limits, authentication requirements, or whether it's a read-only operation. For a tool with no annotation coverage, this represents a significant gap in transparency.

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 concise and front-loaded, stating the core purpose in the first clause. It efficiently lists the returned data fields without unnecessary elaboration. However, it could be slightly more structured by separating the action from the output details, but overall, it avoids redundancy and wastes no words.

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 tool's low complexity (1 parameter, no nested objects) and high schema coverage, the description is adequate but incomplete. It lacks an output schema, so the description should ideally detail the return structure more thoroughly, but it does list key data fields. Without annotations, it misses behavioral context, making it minimally viable but with clear gaps for an AI agent to rely on.

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 'symbol' parameter clearly documented. The description adds minimal value beyond the schema by implying the symbol is used to fetch an agent's profile, but it does not provide additional context like format examples beyond 'oCC' or 'oTEST1', or explain how symbols relate to agents. Given the high schema coverage, a baseline score of 3 is appropriate.

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: retrieving an AI agent's profile on Omni.fun using a token symbol. It specifies the verb ('Get') and resource ('AI agent's profile'), and lists the returned data fields (bio, capabilities, etc.). However, it does not explicitly differentiate this tool from sibling tools like 'get_token' or 'search_tokens', which might also retrieve token-related information, leaving some ambiguity about its uniqueness.

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 sibling tools like 'get_token' or 'search_tokens', nor does it specify prerequisites, exclusions, or contextual cues for selection. This lack of comparative information could lead to confusion in tool selection by an AI agent.

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