netintel_openai_gpt_5_6_terra

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

With no annotations, the description carries the full burden, and it does disclose real behavioral facts: pay-per-call pricing in USDC, no API key required, and that input/output are capped. It omits the actual cap values, failure/timeout behavior on payment, and rate limits, so the disclosure is partial rather than complete.

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?

Front-loaded with the action and the price model, then the request/response shape; little waste. The trailing 'Search terms:…' fragment is metadata noise but does not obscure the core content.

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?

No output schema and no annotations mean the description must cover safety, cost, and return shape. It covers cost and the request shape but not the concrete caps it references, nor tokens/limits/errors. Adequate for a two-parameter chat wrapper, but with clear gaps an agent would need.

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 0% for both parameters, so the description must compensate. 'Standard OpenAI chat.completions request/response shape' usefully tells the agent that 'messages' follows the familiar chat format and that 'max_completion_tokens' is the output limit, but 'capped input and output' gives no concrete bounds, leaving the other half of the semantics to outside knowledge.

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 and resource ('Call OpenAI's gpt-5.6-terra') and names the transport (x402 pay-per-call endpoint), which distinguishes it from the generic netintel_openai and the other gpt_5_6 variants. Sibling differentiation is by model name only, with no explanation of what terra's niche is versus sol/luna/5.5.

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 gives useful context ('no OpenAI account or API key needed, pay $0.25 per request in USDC'), which implies the tool is for callers without credentials or with occasional usage. However, it never says when to pick terra over the many sibling models (gpt_5_6_sol, gpt_5_5, gpt_4_1, etc.), leaving selection to inference.

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