Skip to main content
Glama

chat

Send chat completion requests to local language models via OpenAI-compatible API, with automatic model loading.

Instructions

Send a chat completion request to a model (OpenAI-compatible /v1/chat/completions).

The model is auto-loaded if not already in memory (JIT loading enabled by default in LM Studio). Set stream=True only when the MCP client supports streamed responses; most do not.

Examples: chat(model="qwen/qwen3-4b-2507", messages=[{"role":"user","content":"Hello"}]) chat(model="qwen/qwen3-4b-2507", messages=[{"role":"system","content":"Be terse."},{"role":"user","content":"Hi"}], temperature=0.2, max_tokens=64)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stopNo
modelYes
toolsNo
top_kNo
top_pNo
streamNo
messagesYes
max_tokensNo
temperatureNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations provided; description fully compensates by disclosing JIT auto-loading of models and streaming behavior. It clearly states behavioral traits without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise, well-structured with two paragraphs and examples. Every sentence adds value; no fluff. Front-loaded purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having output schema, description doesn't mention return format. Lacks detail for half the parameters. With many params and no schema descriptions, completeness is insufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so description must explain parameters. It only covers 'model', 'messages', 'temperature', 'max_tokens' via examples, but omits 'stop', 'tools', 'top_k', 'top_p', 'stream' details. Inadequate for 9 parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Send' and resource 'chat completion request', and specifies it is OpenAI-compatible. It distinguishes from siblings like 'complete', 'embed', etc., by focusing on chat completion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides guidance on when to use streaming ('Set stream=True only when the MCP client supports streamed responses; most do not'). However, it does not explicitly compare to alternatives or exclude sibling tools.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/dam2452/llmstudio-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server