list_ollama_models

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

Annotations already declare readOnly, idempotent, and non-destructive, so the bar for additional behavior is lower. The description adds meaningful context: the Ollama prerequisite (localhost:11434 or $OLLAMA_BASE_URL), the associated credit cost, and the 'installed locally' scope. It doesn't cover error behavior, but that is a minor gap for a simple read-only list.

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?

Three sentences, each earning its place: the core operation, the usage guidance, and the prerequisite/cost. The key information is front-loaded and there is no wasted text.

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?

For a zero-parameter, read-only list tool, the description covers purpose, when to use, the alternative, the infrastructure prerequisite, and cost. The output is implied by the purpose ('list... to choose a model'), so nothing needed for correct invocation 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?

There are zero parameters, so the baseline is 4. The description correctly avoids inventing parameter guidance and instead clarifies the output's intended use ('model' value for extract_with_llm), which adds value beyond the empty schema.

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'), a precise resource ('Ollama models installed locally'), and a clear purpose (choosing a model for extract_with_llm). It is distinct from all sibling tools and leaves no ambiguity about what the tool returns.

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?

It explicitly says the tool is not needed before every extraction, names the alternative behavior (extract_with_llm picks a default), and gives two concrete conditions for calling it: a rejected model name or a need for a specific size. This is excellent when-to-use guidance.

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