Fetch all Grok language and image models with live xAI API pricing and release dates. Review input/output token and per-image costs for model selection.
List all Grok language and image models with live pricing from the xAI API.
Returns:
Markdown sections for language models (input/output $/M tokens) and
image generation models ($/image), each with the model's release date.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.0.4Output schema / (root)Previous value: -{
- "properties": {
- "result": {
- "title": "Result",
- "type": "string"
- }
- },
- "required": [
- "result"
- ],
- "title": "list_modelsOutput",
- "type": "object"
-}New value: +nullv1.0.0Input schema / titleAdded value: +"list_modelsArguments"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already provide readOnlyHint=true, and the description adds the return format (Markdown sections with pricing and release dates). No side effects are mentioned, but none are expected. The description complements the annotations well.
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 two short sentences, front-loaded with the action and resource, and every sentence provides valuable information without any fluff.
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 zero parameters, no output schema, and a simple read-only operation, the description fully explains what the tool returns. It is complete for the agent to understand and invoke correctly.
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?
There are no parameters in the input schema, so the description does not need to add parameter-level details. The baseline score of 4 applies as per guidelines for zero parameters.
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 verb 'List' and the resource 'all Grok language and image models' with additional context of live pricing. It distinguishes itself from sibling tools like chat, generate_image, and web_search, which have different purposes.
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 that the tool is for retrieving model information, and the lack of parameters makes it straightforward. While it doesn't explicitly state when to use alternatives, the context is clear enough for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.