list_modelsList available LLM models across OpenAI, Google, Groq, and Ollama. Filter by provider or fetch the latest from APIs to choose the right model for your queries.
List available models for LLM providers
| Name | Required | Description | Default |
|---|---|---|---|
| provider | No | Provider name (optional, lists all if not specified) | |
| fetch_latest | No | Fetch latest models from API vs using cached/configured |
Changes observed during successful MCP inspections.
v1.19.6v1.19.5v1.2.3Input schema / $schemaAdded value: +"http://json-schema.org/draft-07/schema#"Input schema / additionalPropertiesAdded value: +falseInput schema / properties / provider / enumAdded value: +[
+ "openai",
+ "google",
+ "groq",
+ "ollama"
+]v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, covering safety and variability. The description adds 'available models' scope but does not disclose behavioral details like caching defaults or API interaction beyond what the schema implies.
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 a single concise sentence that gets the point across without wasted words. It could be slightly more informative but is not overly verbose.
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 simple nature of the tool with only two optional parameters and full schema descriptions, the description adequately covers the purpose. No output schema is needed for a list operation.
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 100% with both parameters having descriptions. The tool description does not add any meaning beyond the schema, so 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 clearly states the tool's purpose: listing available models for LLM providers. It uses a specific verb 'List' and resource 'models', and distinguishes itself from sibling tools that focus on duck-related operations.
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?
No explicit when-to-use or when-not-to-use guidance is provided. The description implies the tool is for listing models, but does not specify scenarios or alternatives, though no obvious alternatives exist among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.