List Models
get_modelsReturn a list of available models on this API Group: List Models. Billing per call: Credits: metered.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
get_modelsReturn a list of available models on this API Group: List Models. Billing per call: Credits: metered.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description mentions 'Billing per call: Credits: metered', disclosing a cost behavior, which adds context beyond the schema. However, no annotations are provided, and the description does not disclose other behavioral aspects such as whether the list is static or dynamic, pagination, or rate limits. It provides minimal behavioral transparency, but the billing note is a useful addition.
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?
The description is concise, consisting of one sentence plus a billing note. It is front-loaded with the main purpose at the beginning. The billing note adds value and is not redundant, so the structure is efficient with minimal waste.
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?
Given the tool has no parameters, no output schema, and no annotations, the description is short but covers the primary purpose and a key behavioral note (billing). However, it lacks information about the return format (e.g., list of IDs) and potential limitations, which could be improved for a complete understanding. The low complexity might justify a 3, as it is adequate but has minor 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?
The tool has zero parameters, so the schema is fully covered at 100% and there is no parameter detail to add. The description correctly indicates that the tool requires no input, making parameter semantics moot; a baseline of 4 is appropriate for a no-parameter tool.
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 'Return a list of available models on this API Group', using a clear verb and resource. It distinguishes from sibling tools that perform actions like chat, embeddings, and moderations, so the purpose is clear and well-differentiated.
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 provides no guidance on when to use this tool versus alternatives. It does not mention typical use cases such as retrieving model IDs for subsequent API calls, nor does it exclude contexts. The sibling tools imply different actions, but no explicit guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool serves a completely distinct API endpoint: listing models, creating chat completions, generating embeddings, and moderating text. There is no overlap or ambiguity between the operations, making it clear which tool to select for a given task.
All tools follow a consistent verb_noun pattern using HTTP methods (get_ or post_) followed by the resource in snake_case (e.g., get_models, post_chat_completions). The naming is uniform and predictable.
With only 4 tools, the server is concise and well-scoped for its purpose of wrapping a core AI API surface. Each tool covers a fundamental operation (list, chat, embeddings, moderation) without unnecessary bloat.
The toolset covers the primary interactions with a typical AI API: listing models and generating completions, embeddings, and moderation scores. A minor gap is the lack of a single-model GET endpoint, but the current coverage handles core workflows effectively.