find_tensorfeed_data

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

Annotations already declare readOnlyHint, destructiveHint, idempotentHint, and openWorldHint as true/false. The description adds valuable behavioral context: returns 2-3 best-matching endpoints with path, description, and cost; free and no auth; paid endpoints accessible via x402 or credits token. No contradictions.

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 somewhat long but well-structured: it starts with the core purpose, then explains the output and context (many endpoints, paid vs free), and ends with usage advice. Every sentence adds value, though a bit verbose.

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 role as a discovery gateway for 100+ endpoints and no output schema, the description thoroughly covers input, output format, authentication, cost, and use case. It is fully self-contained and actionable.

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 schema already describes both 'query' (plain-language) and 'limit' (max endpoints, default 3). The description restates the default and gives examples but adds no new semantic meaning beyond the schema.

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 uses a specific verb ('discover') and resource ('TensorFeed endpoints') and distinguishes that this is for finding the right endpoint when no dedicated tool fits, clearly differentiating from the many sibling tools.

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 states 'Use this first when no dedicated tool obviously fits,' providing clear when-to-use guidance. Includes examples of plain-language queries, making usage intuitive.

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