Send OpenAI-compatible chat requests with explicit message history, JSON schema and function definitions. Returns tool calls, never executes them.
OpenAI-compatible chat, explicit message history, JSON schema and function definitions. Returns tool calls; never executes them.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| tools | No | ||
| messages | Yes | ||
| max_tokens | No | ||
| temperature | No | ||
| tool_choice | No | ||
| response_format | No |
Changes observed during successful MCP inspections.
v2.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The final clause 'Returns tool calls; never executes them' is a critical behavioral disclosure that the annotations do not provide. It tells the agent that tool calls are returned for external execution, preventing a dangerous assumption that tools are executed automatically. Annotations declare non-readOnly and non-destructive, which aligns with a call that returns data without side effects, but the description adds unique operational context.
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?
A single compact sentence that is front-loaded with the core purpose and ends with the most important behavioral caveat. Every clause earns its place, with 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?
With 7 parameters, 0% schema description coverage, no output schema, and no annotations indicating safety, the description is thin. It covers the key behavior (tool calls are returned, not executed) but doesn't explain the parameters or the expected response format beyond that. It is minimally adequate but has clear gaps.
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% across 7 parameters, so the schema itself provides no semantic help. The description mentions message history, JSON schema, and function definitions, which loosely maps to 'messages' and 'tools'/'response_format', but it doesn't explain parameters like model, max_tokens, temperature, or tool_choice. It partially compensates but leaves many parameters undocumented.
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 states a specific operation: an OpenAI-compatible chat endpoint accepting explicit message history, JSON schema, and function definitions. This clearly separates it from siblings like lm_chat and lm_embeddings. However, it doesn't explicitly state what distinguishes it from lm_chat, which is a likely alternative.
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 when-to-use or when-not-to-use guidance. There is no mention of when to prefer this over lm_chat or other chat-like siblings. It provides implied usage through the feature list, but no explicit routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.