local_llm_models

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

The description discloses that load/unload only works with LM Studio and switches models without GUI, which is useful behavioral context. However, it lacks details on side effects (e.g., memory, time to load), error handling, or whether unloading affects running inference. As annotations are absent, the description carries the full burden and is incomplete.

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 only two sentences, front-loading the core purpose and then clarifying the LM Studio constraint. Every sentence contributes necessary information without redundancy or fluff. Very efficient.

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 the main actions but leaves gaps: it does not describe the return format for 'list' (e.g., JSON array), nor what happens on success/failure for load/unload. With no output schema and three actions, more detail on return values and state changes would improve completeness.

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?

All three parameters are described in the schema (100% coverage). The description adds value by explaining the action enum values ('list' shows models, 'load' loads a model) and providing an example model identifier ('microsoft/phi-4-mini-reasoning'). This supplements the schema beyond redundancy.

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 verb 'List, load, or unload' and the resource 'models on the local LLM server.' It distinguishes the tool from siblings like 'local_llm_run' (which handles inference) and 'llama_server' by specifying model management. The three actions are explicitly listed, making the purpose unambiguous.

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 provides clear context for each action: list shows available models, load/unload switches models. It also notes that load/unload requires LM Studio, implying when not to use these actions. However, it does not explicitly compare to alternatives or give when-not-to-use advice beyond the LM Studio limitation.

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