List the Ollama models installed locally, providing valid model names to use when configuring tasks.
List the Ollama models installed locally and available to these tools.
Use to discover valid values for the model parameter before pinning a
specific model. Returns a list of model-name strings.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool lists locally installed models and returns model-name strings, but it doesn't explicitly state whether it is read-only or mention potential failure modes (e.g., no models installed). For a simple list operation, this is adequate but not exhaustive.
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?
Two concise sentences with no redundancy. The primary action is front-loaded, followed by a clear usage hint and return type. Every sentence earns its place.
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 there are no parameters and the output schema is present (though not shown in the prompt), the description provides the essential information: what it lists, why to use it, and what it returns. Nothing an agent needs to call it correctly is missing.
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 empty and description coverage is 100% by definition. Per the rubric, 0 params earns a baseline of 4. The description adds nothing about parameters because none exist, which 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 states a specific verb ('List') and resource ('Ollama models installed locally'), and clarifies its role as a discovery tool distinct from the sibling task tools (ask_local, chat_local, etc.). It also specifies the return type, making the purpose unambiguous.
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 explicitly says 'Use to discover valid values for the `model` parameter before pinning a specific model,' which provides clear context for when to call it. It doesn't explicitly state when not to use it, but the intent is evident given the sibling tools are the consumers of the model names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.