ai-mldeveloper-tools

LlamaIndex

sammcj

by sammcj

LlamaIndex integrates LlamaIndexTS to deliver AI question answer and code generation with top LLM providers for document

Integrates with LlamaIndexTS to provide access to various LLM providers for code generation, documentation writing, and question answering tasks

github stars

77

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Access to multiple LLM providersDirect file writing capabilitiesBuilt on LlamaIndexTS

best for

  • / Developers needing AI code generation without switching tools
  • / Teams automating documentation creation
  • / Code review and explanation workflows

capabilities

  • / Generate code based on natural language descriptions
  • / Write code directly to specific files and line numbers
  • / Generate documentation for existing code
  • / Ask questions to various LLM providers

what it does

Provides access to multiple LLM providers through LlamaIndexTS for generating code, writing documentation, and answering questions directly from your MCP client.

about

LlamaIndex is a community-built MCP server published by sammcj that provides AI assistants with tools and capabilities via the Model Context Protocol. LlamaIndex integrates LlamaIndexTS to deliver AI question answer and code generation with top LLM providers for document It is categorized under ai ml, developer tools.

how to install

You can install LlamaIndex in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

LlamaIndex is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

MCP LLM

smithery badge

An MCP server that provides access to LLMs using the LlamaIndexTS library.

I put some LLMs in your MCP for your LLMs

<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

call a llm to generate code call a reasoning llm to write some documentation

Installation

Installing via Smithery

To install LLM Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @sammcj/mcp-llm --client claude

Manual Install From Source

  1. Clone the repository
  2. Install dependencies:
npm install
  1. Build the project:
npm run build
  1. Update your MCP configuration

Using the Example Script

The repository includes an example script that demonstrates how to use the MCP server programmatically:

node examples/use-mcp-server.js

This script starts the MCP server and sends requests to it using curl commands.

Examples

Generate Code

{
  "description": "Create a function that calculates the factorial of a number",
  "language": "JavaScript"
}

Generate Code to File

{
  "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

{
  "code": "function factorial(n) {
  if (n <= 1) return 1;
  return n * factorial(n - 1);
}",
  "language": "JavaScript",
  "format": "JSDoc"
}

Ask Question

{
  "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

FAQ

What is the LlamaIndex MCP server?
LlamaIndex is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for LlamaIndex?
This profile displays 50 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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MCP server reviews

Ratings

4.750 reviews
  • Pratham Ware· Dec 28, 2024

    We evaluated LlamaIndex against two servers with overlapping tools; this profile had the clearer scope statement.

  • Ishan Ndlovu· Dec 20, 2024

    We evaluated LlamaIndex against two servers with overlapping tools; this profile had the clearer scope statement.

  • Ira Sharma· Dec 20, 2024

    Useful MCP listing: LlamaIndex is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Jin Verma· Dec 8, 2024

    According to our notes, LlamaIndex benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Jin Abbas· Nov 27, 2024

    LlamaIndex is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Yash Thakker· Nov 19, 2024

    LlamaIndex has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Kaira Desai· Nov 11, 2024

    LlamaIndex has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Soo Smith· Nov 11, 2024

    LlamaIndex is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Jin Ramirez· Oct 18, 2024

    We evaluated LlamaIndex against two servers with overlapping tools; this profile had the clearer scope statement.

  • Dhruvi Jain· Oct 10, 2024

    According to our notes, LlamaIndex benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

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