llm_models

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

The description discloses that remove deletes a model from disk and pull is slow, which is valuable behavioral context. However, it directly contradicts the annotations' destructiveHint=false, since removal is inherently destructive. This inconsistency is a severe transparency failure.

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 well-structured with a one-line summary followed by a bulleted list of operations. Each sentence provides necessary information with no fluff, making it appropriately sized and front-loaded.

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?

The description covers all three operations, parameter requirements, performance implications, and the scope (Ollama and LM Studio). Given the tool's moderate complexity and the presence of an output schema, it is nearly complete, though edge cases like unreachable API URLs are not addressed.

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?

Schema coverage is 100%, so all three parameters (operation, model_name, ollama_url) are already described in the schema. The description adds little beyond a performance note for pull and restates the model_name requirement, which the schema already covers. Baseline 3 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 clearly states the tool as a 'Portmanteau' for list, pull, or remove Ollama models, with specific verbs (list, pull, remove) and resource (Ollama models). It enumerates operations and details each one, distinguishing it from sibling list_local_models by including pull and remove actions.

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 offers operation-specific guidance (e.g., model_name required for pull/remove, pull is slow for large models) which implies when each operation should be used. However, it does not explicitly discuss alternatives, such as when to prefer sibling list_local_models for simple listing, so usage exclusions are missing.

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