iris

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

No annotations are provided, so the description carries the full burden. It discloses real behavioral surface — web search, optional code interpreter, model choice, natural language input — but says nothing about cost, latency, network dependence, non-determinism, or the fact that this spawns a full external LLM agent. Those are exactly the traits an agent needs before invoking it.

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 and compact at four sentences, with capability stated first and supporting details after. The version tag '(v0.6.1)' and the closing 'Supports natural language queries' are minor filler that adds no decision value.

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?

Six parameters, no output schema, no annotations. The description covers capabilities but omits the conversational-continuity behavior (previous_response_id) and any resource/cost expectations, which matter for a 6-param agent tool. It is adequate but leaves an agent without key operational context.

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 description coverage is 100% and three parameters carry enums with defaults, so the schema already does the semantic work. The description restates model options (gpt-5.1/gpt-5/o3) without adding format or selection guidance beyond the schema. Baseline 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 names specific capabilities — an AI agent doing web search and code execution with model selection and an optional code interpreter. That is a concrete verb+resource picture, not a tautology of the opaque name 'iris'. The only gap is the absence of siblings to differentiate against (there are none), so no routing language is needed.

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?

It lists use cases (latest information, troubleshooting errors, executing code), which implies when to reach for it, but gives no explicit when-not-to-use or alternatives. With no sibling tools present, the missing routing guidance is a smaller gap, but the 'when should I not call this' side is entirely unstated.

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