Run chat, vision, and stats inference on local LM Studio models. Store responses by ID to continue conversations, and use explicitly enabled MCP plugins.
Native chat/vision/stats. store=true returns response_id for continuation. Optional explicitly enabled LM Studio MCP plugins.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | ||
| model | Yes | ||
| options | No | ||
| integrations | No |
Changes observed during successful MCP inspections.
v2.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as destructive, open-world, and not read-only. The description adds useful context about the store=true continuation behavior and optional MCP plugins, but it does not explain what gets destroyed, how plugins are authorized, or what side effects to expect from a destructive chat tool.
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 short and front-loaded, which is good, but it is fragmented and under-specified. The first sentence is vague, and the remaining two sentences cover only narrow aspects of the tool.
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?
For a tool with nested chat options, required model and input parameters, optional integrations, and no output schema, the description is substantially incomplete. It does not explain input structure, available options, expected return values, or integration requirements.
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 0%, so the description must explain the four parameters. It only touches on store=true and optional MCP plugins while saying nothing about model, input format (string vs array), or the many options fields like temperature, context_length, and previous_response_id.
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 phrase 'Native chat/vision/stats' gives a general idea that this is a local model chat tool with vision and stats support, but it lacks a clear verb and does not distinguish itself from siblings like lm_openai_chat or lm_rag_ask. An agent must guess what 'native' means and whether this is the right chat tool.
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 offers no guidance on when to use lm_chat versus lm_openai_chat, lm_rag_ask, or other siblings. It mentions store=true and MCP plugins, but these are behavioral notes, not usage conditions or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.