LLM Broker

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

Annotations declare readOnlyHint=false and openWorldHint=true, so the safety profile is partly covered. The description adds genuinely new behavioral context: the Authorization header format and that calls are billed like the REST API. It does not disclose rate limits, error behavior, or the shape of the returned completion.

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?

Four dense sentences, front-loaded with what the tool does before moving to model defaults, routing, and auth. Nothing is redundant, though the routing example and auth note are packed tightly without much separation.

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 nested-object, open-world generation call with no output schema, the description covers model selection, routing, and billing but omits the return format, how `messages` vs `prompt` interact, and any constraints on max_tokens. Functional but with real gaps.

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 only 20%, with just `prompt` documented inline, so the description must compensate. It clarifies `model` (default taylor, ids from list_models) and gives a concrete `routing` example, but leaves `messages`, `max_tokens`, and the routing schema itself unelaborated.

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 states a specific verb and resource: sends messages to the broker using OpenAI chat completions. It references list_models as the source of model ids, which implicitly frames the sibling relationship, but never explicitly contrasts the two tools' roles.

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 gives useful invocation context (model defaults to taylor, routing accepts criteria per request), but never says when to choose this tool over list_models or what a typical flow looks like. Usage is implied through the parameter hints rather than stated.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered; the description still adds real context beyond them — the default USD currency, the pointer to GET /api/v1/broker/currencies for other currencies, and the deterministic sort rule (score within category, otherwise price). It omits pagination/limit behavior, which is the main remaining gap.

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?

A single dense sentence that front-loads the payload (models, scores, prices, regions) before the filtering and sorting rules. Every clause carries information, though the parenthetical currency endpoint reference slightly interrupts the flow.

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 exists, so the description must describe the return shape, and it does list the per-record fields (category scores, price per 1M tokens, regions) plus the ordering. It stops short of explaining pagination or whether limit caps the result set, which an agent would want for a listing tool.

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 only 40% (limit, region and category are bare), and the description compensates by naming the three filter dimensions (category, region, price) and clarifying the currency param's default and how to discover valid codes. It adds marginal value on top of the schema but leaves limit semantics (page size vs. total) unspecified.

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 (list) and resource (models on the broker) and enumerates exactly what each record carries: per-category benchmark scores (0-1, measured by the provider), price per 1M tokens with currency, and regions. The only sibling, chat, is unrelated, so differentiation is trivially satisfied.

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 implies usage ('Filter by category, region and price') and explains the ordering rule, but never states when to reach for this tool versus not, nor any prerequisite conditions. With a single unrelated sibling there is little to disambiguate, so implied guidance is adequate but not explicit.

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