local_llm_run

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the tool is free, private, requires no API key, and supports streaming via 'stream=true'. It does not mention potential side effects or prerequisites, but for a read-like operation this is adequate.

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 extremely concise—two sentences achieving high information density. The first sentence states purpose and key attributes; the second clarifies when to use and a streaming option. No wasted words.

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 medium complexity (8 parameters, no output schema), the description covers purpose, usage scope, and a key optional feature (streaming). It provides enough context for an agent to decide and invoke correctly, though lacking details on error handling or response format.

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 baseline is 3. The description adds little beyond the schema, only reiterating the streaming hint and the default behavior for model/endpoint. No additional parameter context or examples are provided.

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 sends a prompt to a local LLM, explicitly listing supported backends (LM Studio, Ollama, etc.) and use cases (classify, format, extract, rewrite, proofread). It differentiates from siblings like claude_run, gemini_run, and llama_server by emphasizing 'Free, private, no API key needed' and 'Best for simple tasks.'

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 tells the agent this tool is 'Best for simple tasks' and lists specific task types, giving clear guidance on when to use it. However, it does not explicitly state when NOT to use it or suggest alternative tools for complex tasks, which would improve the score further.

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