mcp-llm
by sammcj
# MCP LLM
An MCP server that provides access to LLMs using the LlamaIndexTS library.
## Features
This MCP server provides the following tools:
- `generate_code`: Generate code based on a description
- `generate_code_to_file`: Generate code and write it directly to a file at a specific line number
- `generate_documentation`: Generate documentation for code
- `ask_question`: Ask a question to the LLM
## Installation
Update your MCP config to add the mcp-llm server:
```json
{
"mcpServers": {
"llm": {
"command": "npx",
"args": [
"-y",
"mcp-llm"
],
"env": {
"LLM_MODEL_NAME": "deepseek-r1:7b-qwen-distill-q6_k_l",
"LLM_MODEL_PROVIDER": "ollama",
"LLM_BASE_URL": "http://localhost:11434",
"LLM_ALLOW_FILE_WRITE": "true",
"LLM_TIMEOUT_S": "240"
},
"disabled": false,
"autoApprove": [
"generate_code",
"generate_documentation",
"ask_question",
"generate_code_to_file"
],
"timeout": 300
},
}
}
```
## Available Scripts
- `npm run build` - Build the project
- `npm run watch` - Watch for changes and rebuild
- `npm start` - Start the MCP server
- `npm run example` - Run the example script
- `npm run inspector` - Run the MCP inspector
## Configuration
The MCP server is configurable using environment variables:
### Required Environment Variables
- `LLM_MODEL_NAME`: The name of the model to use (e.g., `qwen2-32b:q6_k`, `anthropic.claude-3-7-sonnet-20250219-v1:0`)
- `LLM_MODEL_PROVIDER`: The model provider (e.g., `bedrock`, `ollama`, `openai`, `openai-compatible`)
### Optional Environment Variables
- `LLM_BASE_URL`: Base URL for the model provider (e.g., `https://ollama.internal`, `http://my-openai-compatible-server.com:3000/v1`)
- `LLM_TEMPERATURE`: Temperature parameter for the model (e.g., `0.2`)
- `LLM_NUM_CTX`: Context window size (e.g., `16384`)
- `LLM_TOP_P`: Top-p parameter for the model (e.g., `0.85`)
- `LLM_TOP_K`: Top-k parameter for the model (e.g., `40`)
- `LLM_MIN_P`: Min-p parameter for the model (e.g., `0.05`)
- `LLM_REPETITION_PENALTY`: Repetition penalty parameter for the model (e.g., `1.05`)
- `LLM_SYSTEM_PROMPT_GENERATE_CODE`: System prompt for the generate_code tool
- `LLM_SYSTEM_PROMPT_GENERATE_DOCUMENTATION`: System prompt for the generate_documentation tool
- `LLM_SYSTEM_PROMPT_ASK_QUESTION`: System prompt for the ask_question tool
- `LLM_TIMEOUT_S`: Timeout in seconds for LLM requests (e.g., `240` for 4 minutes)
- `LLM_ALLOW_FILE_WRITE`: Set to `true` to allow the `generate_code_to_file` tool to write to files (default: `false`)
- `OPENAI_API_KEY`: API key for OpenAI (required when using OpenAI provider)
## Manual Install From Source
1. Clone the repository
2. Install dependencies:
```bash
npm install
```
3. Build the project:
```bash
npm run build
```
4. Update your MCP configuration
### Using the Example Script
The repository includes an example script that demonstrates how to use the MCP server programmatically:
```bash
node examples/use-mcp-server.js
```
This script starts the MCP server and sends requests to it using curl commands.
## Examples
### Generate Code
```json
{
"description": "Create a function that calculates the factorial of a number",
"language": "JavaScript"
}
```
### Generate Code to File
```json
{
"description": "Create a function that calculates the factorial of a number",
"language": "JavaScript",
"filePath": "/path/to/factorial.js",
"lineNumber": 10,
"replaceLines": 0
}
```
The `generate_code_to_file` tool supports both relative and absolute file paths. If a relative path is provided, it will be resolved relative to the current working directory of the MCP server.
### Generate Documentation
```json
{
"code": "function factorial(n) {\n if (n <= 1) return 1;\n return n * factorial(n - 1);\n}",
"language": "JavaScript",
"format": "JSDoc"
}
```
### Ask Question
```json
{
"question": "What is the difference between var, let, and const in JavaScript?",
"context": "I'm a beginner learning JavaScript and confused about variable declarations."
}
```
## License
- [MIT LICENSE](LICENSE)