LLM Chat MCP server
Enables agents to inspect configured LLM models and send chat requests to them, with support for parameter overrides, file-based prompts, and output to files.
README
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
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 --help
| Argument | Description | Default |
|---|---|---|
--config PATH |
Path to config.yaml | ~/.continue/config.yaml |
--default-model NAME |
Default model for send_request | (none) |
--timeout SECONDS |
Request timeout in seconds | 19 |
--relative_paths_base PATH |
Base directory for resolving relative output file paths | (process cwd) |
--auto-output-dir PATH |
Directory for auto-generated output files when response exceeds auto_file_threshold and no output_file_path is set |
(OS temp directory) |
Configuration
- Config is re-read on every request — no restart needed
- Auth uses
apiKeyfrom each model entry in config.yaml - If 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 |
|---|---|---|---|
model_selector |
str | (CLI default) | Model name or id from config.yaml |
prompt_text |
str | (none) | Prompt text to send |
prompt_files |
list | (none) | List of file specs (string path or {path, start_line, end_line} dict) |
include_line_numbers |
bool | true |
Prefix each file line with N: |
system_prompt |
str | (from config) | Override system message; "" suppresses entirely |
temperature, topP, topK, minP |
float | (from config) | Sampling parameters |
maxTokens, presencePenalty, frequencyPenalty |
int/float | (from config) | Generation parameters |
extraParams |
dict | (none) | Extra body properties merged into API request |
details |
bool | false |
Include reasoning/thinking content in response |
timeout |
float | (CLI default) | Per-request timeout override |
output_file_path |
str | (none) | Write full response to this file (relative paths resolved against --relative_paths_base) |
append |
bool | false |
Append to output_file_path instead of overwriting; returns appended_line_start/appended_line_end |
inline_preview_chars |
int | 500 |
Max chars of content returned inline; preview applies only when a file is written |
auto_file_threshold |
int | 8000 |
Auto-write response to file when response_chars exceeds this and no output_file_path is set; 0 disables |
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_file
Testing
python tests/test_output_strategies.py
Tests 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.
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