LocalLLaMA community clarifies LLM tool use

Tool use is the critical bridge from "chatting" to "acting," but it remains a double-edged sword for local LLM users.

• –Tools provide deterministic capabilities (calculators, APIs) to non-deterministic models, solving the "reasoning vs. retrieval" gap.
• –Over-tooling can bloat the prompt, significantly increasing noise and reducing the effective context window for the actual task.
• –Security remains a major concern: a model with file-system or browser access can theoretically be manipulated via prompt injection to leak data.
• –Model performance is a bottleneck; smaller models often fail to generate the precise JSON structure required for successful function calls.
• –For local setups, tools are typically defined in the inference engine (like Ollama or vLLM) and presented to the model as JSON schemas.