local_llm_chat

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

With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal insight. It states the tool chats with a model but doesn't describe response format, error handling, rate limits, or whether it maintains conversation state. For a chat tool with zero annotation coverage, this is a significant gap in transparency about how the tool behaves beyond basic functionality.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place by conveying essential information concisely.

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 complexity of a chat tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (e.g., text response, structured data), error conditions, or dependencies like server status. For a tool that likely involves network calls and model interactions, more context is needed to guide effective use.

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 description coverage is 100%, so the schema fully documents all three parameters (message, model, temperature) with their types, descriptions, and defaults. The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints. Baseline 3 is appropriate when the schema handles all parameter documentation.

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 action ('Chat with') and resource ('a local Ollama model'), making the purpose immediately understandable. It distinguishes from siblings like 'list_local_models' or 'remove_model' by focusing on interactive conversation rather than management tasks. However, it doesn't specify the exact scope (e.g., single-turn vs. multi-turn) or differentiate from 'select_chat_model' which might have overlapping functionality.

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?

No explicit guidance is provided on when to use this tool versus alternatives like 'select_chat_model' or 'test_model_responsiveness'. The description implies usage for general chat interactions but doesn't mention prerequisites (e.g., server running), exclusions, or comparative scenarios. This leaves the agent to infer context from tool names alone.

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