list_modelsQuery all available AI models across configured providers. It returns each model's ID, name, provider, and capabilities, so you can pick the right one for any task.
List all available AI models from all configured providers
Returns: List of model information including ID, name, provider, and capabilities
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden, but this is a zero-parameter read-only listing with minimal risk. It discloses scope ('all configured providers') and the fields returned, yet says nothing about freshness, permissions, or whether unconfigured providers are surfaced.
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 action in one sentence, followed by a compact return summary. The Returns block partially duplicates the output schema, which costs a little, but nothing is bloated.
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?
With an output schema present, the description need not explain return values, and the operation is a simple no-arg list. It is nearly complete, missing only a note on how results should be used or how current the model list is.
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 takes zero parameters, so the baseline is 4; there is no parameter semantics to add meaning to and no schema coverage gap to compensate for.
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 ('List all available AI models') and adds provider scope ('from all configured providers'). It is clear without naming a sibling, and no sibling (compare, analyze, chat, generate) overlaps in purpose, so explicit differentiation isn't 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?
There is no statement of when to use this tool versus the siblings or in what workflow context it belongs (e.g., before chat/generate to discover valid model IDs). Usage is only weakly implied by 'list all available.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.