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

No annotations are provided, so the description carries the full burden, and it does so thoroughly: it discloses the return shape (2-3 endpoints, each with HTTP path, what it returns, free/paid), the auth profile ('Free, no auth'), the access mechanisms for paid endpoints (x402 or credits token), and the scope of the catalog (100+ endpoints, plus unlisted callable tools). These are exactly the traits an agent needs and that no structured field supplies.

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?

Purpose and routing advice are front-loaded, which is correct. The prose is dense and slightly run-on across the access-mechanism clauses, but virtually every sentence earns its place by adding routing or access information rather than restating the schema.

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?

For a discovery tool with no output schema and no annotations, the description is complete: it explains the query input, the number and content of returned matches, and the auth/paid-access model. Nothing an agent needs to invoke it correctly is missing.

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 documents both parameters, making 3 the baseline. The description adds value beyond that by giving concrete query examples ('trending AI papers', 'is OpenAI down', 'model price history') that clarify the intended plain-language input, though it adds nothing further about the limit parameter.

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?

States a specific verb+resource ('Discover which TensorFeed endpoint answers a data need') and immediately distinguishes itself from the many sibling data tools by positioning as the discovery/router layer. An agent can tell what it returns (2-3 best-matching endpoints) without opening the schema, and can tell it apart from the concrete siblings like get_research_papers or status_uptime.

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 the selecting condition: 'Use this first when no dedicated tool obviously fits,' and explains why (core endpoints are dedicated tools, the full catalog is reachable here). This gives a clear when-to-use rule against the concrete alternatives in the sibling list.

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