LLM Chat MCP server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@LLM Chat MCP serverAsk the default model to explain what MCP is."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
llm-chat-mcp
MCP server for interacting with LLM models configured in Continue.dev's config.yaml.
What it does
Exposes 4 tools that let the agent inspect your configured models and send chat requests to them:
llm_chat_list_models— list all models in your config with their names and idsllm_chat_get_model_params— inspect parameters (temperature, topP, etc.) for any modelllm_chat_get_model_prompt— read the system prompt configured for a modelllm_chat_send_request— chat with a model, optionally overriding parameters or loading prompts from files
Related MCP server: Polybrain MCP Server
Installation
pip install -e .Or run directly from source without install.
CLI Usage
python -m llm_chat_mcp --default-model "GLM-5.2-FP8"
python -m llm_chat_mcp --config /path/to/config.yaml --default-model "ModelName"
python -m llm_chat_mcp --helpArgument | Description | Default |
| Path to config.yaml |
|
| Default model for send_request | (none) |
| Request timeout in seconds |
|
| Base directory for resolving relative output file paths | (process cwd) |
| Directory for auto-generated output files when response exceeds | (OS temp directory) |
Configuration
Config is re-read on every request — no restart needed
Auth uses
apiKeyfrom each model entry in config.yamlIf no model is specified (neither in CLI nor tool call), an error tells you how to set one
llm_chat_send_request parameters
The main tool. Sends a chat completion request to an LLM and returns the response.
Input parameters
Parameter | Type | Default | Description |
| str | (CLI default) | Model name or id from config.yaml |
| str | (none) | Prompt text to send |
| list | (none) | List of file specs (string path or |
| bool |
| Prefix each file line with |
| str | (from config) | Override system message; |
| float | (from config) | Sampling parameters |
| int/float | (from config) | Generation parameters |
| dict | (none) | Extra body properties merged into API request |
| bool |
| Include reasoning/thinking content in response |
| float | (CLI default) | Per-request timeout override |
| str | (none) | Write full response to this file (relative paths resolved against |
| bool |
| Append to |
| int |
| Max chars of content returned inline; preview applies only when a file is written |
| int |
| Auto-write response to file when |
Response structure
Always returned as JSON:
{
"content": "<preview, full content, or empty>",
"truncated": true,
"metadata": {
"model_name": "...",
"model": "...",
"elapsed_seconds": 1.23,
"request_sent": {...},
"response_headers": {...},
"response_chars": 1234,
"output_file": "...",
"auto_output_file": "...",
"created_dirs": [...],
"appended_line_start": 201,
"appended_line_end": 250
}
}Output strategy
The tool chooses one of three strategies based on parameters and response size:
Explicit file (
output_file_pathset): full response written to the file. Ifappend=true, the response is appended andappended_line_start/appended_line_end(1-based, inclusive) are returned so the caller can read only the appended slice viaextract_lines.Auto file (no
output_file_path,auto_file_threshold > 0,response_chars > threshold): full response written to an auto-generated file in--auto-output-dir(or OS temp). Filename format:llm_output_<YYYYMMDD_HHMMSS>_<6-char-uuid>.json.Inline only (no file written): full content returned in the
contentfield.
Inline preview
When a file is written (explicit or auto), the content field contains a preview of the response:
If
inline_preview_chars > 0andlen(content) > inline_preview_chars: truncated preview with suffix[truncated, full response in <file_path>].If
inline_preview_chars > 0andlen(content) <= inline_preview_chars: full content (fits in preview).If
inline_preview_chars == 0: emptycontent(file has the full response).
When no file is written, the full content is returned inline regardless of inline_preview_chars — this prevents data loss.
Continue.dev Integration
Add to .continue/mcpServers/llm-chat.yaml:
name: LLM Chat MCP server
version: 0.2.0
schema: v1
mcpServers:
- name: LLM Chat MCP server
command: python
args:
- "-m"
- "llm_chat_mcp"
- "--default-model"
- "GLM-5.2-FP8"
- "--timeout"
- "570"
- "--relative_paths_base"
- "/path/to/your/workspace"
- "--auto-output-dir"
- "/path/to/your/workspace/.continue/skills/large-tasks/tmp-outputs"
env:
PYTHONPATH: "/path/to/llm-chat-mcp"Then reload Continue.dev.
Project Structure
llm-chat-mcp/
├── pyproject.toml # Dependencies: mcp, pyyaml, httpx
├── README.md # This file
├── llm_chat_mcp/
│ ├── __init__.py
│ ├── __main__.py # CLI entry point + tool registration
│ ├── config.py # Config loading, model resolution
│ └── api.py # API client, error handling
└── tests/
└── test_output_strategies.py # Tests for append, inline_preview, auto_fileTesting
python tests/test_output_strategies.pyTests mock the API call and verify the file-writing and response assembly logic. Covers all combinations of output_file_path, append, inline_preview_chars, and auto_file_threshold.
This server cannot be deployed
Maintenance
Related MCP Connectors
Provides capabilities that let LLM agents perform a range of infrastructure management tasks.
LLM chat, text tools, image generation, editing, batch image jobs, and asynchronous video generation
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Manage portable AI agent playbooks, Agent Skills, MCP configurations, personas, and memory.
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