netintel_openai_gpt_5_6_luna

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

With no annotations, the description carries the full burden and does meaningful work: it discloses the auth model (no OpenAI account/API key), the billing side effect ($0.06 per request in USDC), and the response shape (standard chat.completions). It underspecifies what 'capped input and output' concretely means and omits rate limits.

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 core operation and cost, tightly worded. The trailing truncated 'Search terms:…' fragment is dead weight/artifact but overall the payload is compact.

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 2-param, no-output-schema, no-annotation tool the description covers auth, cost, and request shape adequately. But it omits the single most important disambiguator given 14 model siblings—what this model is for relative to them—leaving a real gap in call correctness.

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% and there are 2 undocumented params, so the description must compensate. 'Standard OpenAI chat.completions request/response shape' implicitly documents the messages structure, but max_completion_tokens is only hinted at by 'capped output' and the 'capped input' limit is never quantified.

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-luna) and notes the transport (x402 pay-per-call endpoint). However, it does not differentiate this model from the ~14 sibling netintel_openai_gpt_* tools (sol, terra, 5.5, 5.4, etc.), so an agent cannot tell why 'luna' is the right choice.

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?

Gives payment/logistics context (no API key, $0.06 in USDC) but no when-to-use guidance, no conditions distinguishing it from the other gpt model siblings, and no exclusions. The agent is left to infer model-selection criteria entirely.

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