list_running_models

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

With no annotations provided, the description itself must convey behavioral traits. The verb 'list' implies a read-only operation, but the description does not explicitly state that it has no side effects or require any special permissions. It does clarify that it refers to memory status, but lacks additional behavioral context beyond the basic function.

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 a single, concise sentence that directly states the tool's function. It is front-loaded with the action verb and contains no unnecessary information or repetition.

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's simplicity (zero parameters) and the presence of an output schema, the description sufficiently covers the essential context. It clarifies what 'running' means (in memory) and distinguishes from local models, making it complete for an agent to invoke correctly.

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 input schema is complete. The description does not need to explain parameters, and it appropriately focuses on the action. Baseline 4 is appropriate for tools with no parameters.

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 uses the specific verb 'list' and clearly identifies the resource as 'all models currently loaded and running in memory (RAM/VRAM).' This clearly distinguishes it from sibling tools like list_local_models by specifying the in-memory scope.

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 that it lists models that are currently loaded and running in memory, which implies the appropriate use case. It does not explicitly mention alternatives or exclusions, but the context is clear enough to guide the agent in selecting this tool over list_local_models.

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