mcp-llm
# MCP LLM
[](https://smithery.ai/server/@sammcj/mcp-llm)
An MCP server that provides access to LLMs using the LlamaIndexTS library.
<a href="https://glama.ai/mcp/servers/i1gantlfrs">
<img width="380" height="200" src="https://glama.ai/mcp/servers/i1gantlfrs/badge" alt="mcp-llm MCP server" />
</a>
## 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
### Installing via Smithery
To install LLM Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@sammcj/mcp-llm):
```bash
npx -y @smithery/cli install @sammcj/mcp-llm --client claude
```
### 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)TDQS
Scored across 4 tools
The tools have overlapping purposes that could cause confusion. 'generate_code' and 'generate_code_to_file' both generate code, with the latter adding file writing functionality, which might lead to misselection when file output isn't needed. However, 'ask_question' and 'generate_documentation' are more distinct in their purposes, helping to mitigate some ambiguity.
The naming follows a consistent verb_noun pattern throughout, such as 'ask_question' and 'generate_documentation'. There is a minor deviation with 'generate_code_to_file', which includes a prepositional phrase, but overall the pattern is clear and readable, maintaining good consistency.
With 4 tools, the count is slightly low but reasonable for an LLM-focused server. It covers core functionalities like questioning, code generation, and documentation, though it might feel thin if more advanced features are expected. The scope is well-defined, so the number is appropriate for basic operations.
There are notable gaps in the tool surface for an LLM domain. While it covers code generation and documentation, it lacks tools for editing, refactoring, or analyzing existing code, and there's no way to manage conversations or context. This could lead to agent failures in more complex workflows, but core tasks are addressed.