List Ollama models

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It clearly states that the tool lists models, delegation mode, policy, and env vars, and that settings are user-controlled. It also warns that there is no tool to change them and instructs the agent not to edit config files. This covers the read-only nature and constraints. However, it does not mention any potential side effects, error conditions, or details about the output format, which would have made it a 5.

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 four sentences, tightly packed with information. The first sentence gives the purpose, the second gives usage guidance, and the last two provide behavioral context and alternative actions. No redundant sentences or filler. It is well-structured and front-loaded, making it easy for an AI agent to parse quickly.

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 tool has no parameters, no output schema, and no annotations, the description provides a solid overview of what the tool returns and how to use it. However, it lacks a brief description of the output format (e.g., whether it returns a list of model names or a structured JSON). This is a minor gap, but overall the description is sufficient for the agent to understand the tool's purpose and constraints.

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, and schema description coverage is 100% (no parameters to describe). According to the guidelines, this gives a baseline of 4. The description does not add parameter semantics because there are none, and it correctly avoids adding unnecessary info. The score 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 starts with a specific verb 'List' and clearly identifies the resources: Ollama models, delegation mode, allowed-model policy, and environment variables. It distinguishes from sibling tools which are about delegation actions (start, cancel, followup, etc.) by focusing on listing models and configuration. This leaves no ambiguity about what the tool does.

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 states when to use the tool: 'Use this to pick a model, or to report the current configuration when the user asks about it.' It also provides crucial guidance on what not to do: there is no tool to change settings, and the agent should tell the user to run 'ollama-mcp-config' in a terminal and restart the session, and not to edit config files. This is exceptional for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.