list_models
List available models from Ollama Cloud API, automatically rotating accounts to handle rate limits and quota exhaustion.
Instructions
List available models
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
List available models from Ollama Cloud API, automatically rotating accounts to handle rate limits and quota exhaustion.
List available models
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden of behavioral disclosure. It only says 'List available models' with no mention of sorting, filtering, rate limits, or any side effects. For a zero-parameter tool, minimal transparency is acceptable, but the description adds no value beyond the name.
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 extremely concise—one short phrase. It is front-loaded and easy to parse. However, it could be slightly more structured (e.g., 'Lists all models that are currently available for use.') without losing conciseness.
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 no output schema, the description should explain what the return value contains. It does not. For a listing operation, users need to know if it returns model IDs, names, metadata, etc. The description is incomplete for a tool with no output schema.
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 zero parameters, so the baseline score is 4. The description adds meaning by specifying that the tool lists 'available models', which clarifies what is returned. No additional parameter details are needed.
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 action ('List') and resource ('available models'). It is a specific verb+resource pair. However, it does not differentiate from sibling tools like 'get_account_status' or 'get_metrics', which also retrieve information. Still, the purpose is clear.
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. There is no mention of context, prerequisites, or when not to use it. As a simple listing tool, it might be self-evident, but the absence of any usage instructions lowers the score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/irad-bouzidi/mcp-ollama-account-rotation-new'
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