list_loaded_models

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It states the tool lists models currently in memory, but it does not disclose return format (e.g., IDs, names), whether the list is ordered, or how empty states are handled. This is a minimum viable description with clear gaps.

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, front-loaded sentence with no redundant words. Every word earns its place, making it highly concise and well-structured.

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 (no parameters, no output schema), the description is nearly complete for an agent to understand its purpose. However, it could briefly mention what the returned list contains (e.g., model IDs or names) to fully round out the context.

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 a baseline of 4 is appropriate per the rubric. The description adds no parameter-specific details because none exist, but it does provide context that the list covers all loaded models, which contributes to overall semantics.

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 a specific verb+resource combination ('List all IFC models currently loaded in memory') that clearly identifies the tool's function. It distinguishes itself from sibling tools like load_ifc_file and unload_ifc_file by focusing on querying the current state.

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 implies usage by indicating it lists loaded models, but it does not explicitly state when to use this tool versus alternatives or provide any exclusions. It does not mention related tools like load_ifc_file or unload_ifc_file, so guidance is only implicit.

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