What can you do with this server?

This server provides access to multiple Large Language Model (LLM) APIs including ChatGPT, Claude, and DeepSeek through a Model Context Protocol (MCP) interface, along with Bitcoin and Lightning network operations. **LLM Capabilities:** * **Call individual LLMs**: Use tools like `call-chatgpt`, `call-claude`, and `call-deepseek` to send prompts to specific AI providers with configurable parameters like model, temperature, and token limits * **Combine LLM responses**: Use `call-all-llms` to send the same prompt to all available LLMs simultaneously and receive combined output with individual responses and a summary * **Dynamic provider selection**: Use `call-llm` to select an LLM provider ("chatgpt", "claude", or "deepseek") at runtime * **Compare model outputs**: Facilitate multi-perspective analysis, model comparison, and quality assurance **Bitcoin & Lightning Network Features:** * Generate new Bitcoin key pairs and addresses * Validate Bitcoin addresses * Decode raw Bitcoin transactions from hexadecimal * Retrieve latest Bitcoin block information * Get specific Bitcoin transaction details using transaction ID * Decode BOLT11 Lightning invoices * Pay BOLT11 Lightning invoices **Configuration**: Set environment variables for API keys (OPENAI\_API\_KEY, ANTHROPIC\_API\_KEY, DEEPSEEK\_API\_KEY) and default models for each provider.

Which integrations are available for this server?

Used for managing environment variables including API keys and default model configurations for the various LLM providers. Provides access to [OpenAI](/mcp/servers/integrations/openai)'s ChatGPT API for generating responses from various GPT models with customizable parameters for temperature and token limits. Implements schema validation for tool parameters to ensure proper formatting of requests to the different LLM APIs.

Cross-LLM MCP Server

Trust Score

A Model Context Protocol (MCP) server that provides access to multiple Large Language Model (LLM) APIs including ChatGPT, Claude, DeepSeek, Gemini, Grok, Kimi, Perplexity, and Mistral. This allows you to call different LLMs from within any MCP-compatible client and combine their responses.

Features

This MCP server offers:

Eight specialized tools for interacting with different LLM providers
User preference system with tag-based model selection
Model tagging to identify models by their strengths (coding, business, reasoning, etc.)
Cost preference settings to favor flagship or cheaper models

🤖 Individual LLM Tools

`call-chatgpt`

Call OpenAI's ChatGPT API with a prompt.

Input:

prompt (string): The prompt to send to ChatGPT
model (optional, string): ChatGPT model to use (default: gpt-4)
temperature (optional, number): Temperature for response randomness (0-2, default: 0.7)
max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

ChatGPT response with model information and token usage statistics

Example:

ChatGPT Response Model: gpt-4 Here's a comprehensive explanation of quantum computing... --- Usage: - Prompt tokens: 15 - Completion tokens: 245 - Total tokens: 260

`call-claude`

Call Anthropic's Claude API with a prompt.

Input:

prompt (string): The prompt to send to Claude
model (optional, string): Claude model to use (default: claude-3-sonnet-20240229)
temperature (optional, number): Temperature for response randomness (0-1, default: 0.7)
max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

Claude response with model information and token usage statistics

`call-deepseek`

Call DeepSeek API with a prompt.

Input:

prompt (string): The prompt to send to DeepSeek
model (optional, string): DeepSeek model to use (default: deepseek-chat)
temperature (optional, number): Temperature for response randomness (0-2, default: 0.7)
max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

DeepSeek response with model information and token usage statistics

`call-gemini`

Call Google's Gemini API with a prompt.

Input:

prompt (string): The prompt to send to Gemini
model (optional, string): Gemini model to use (default: gemini-2.5-flash)
temperature (optional, number): Temperature for response randomness (0-2, default: 0.7)
max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

Gemini response with model information and token usage statistics

`call-grok`

Call xAI's Grok API with a prompt.

Input:

prompt (string): The prompt to send to Grok
model (optional, string): Grok model to use (default: grok-3)
temperature (optional, number): Temperature for response randomness (0-2, default: 0.7)
max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

Grok response with model information and token usage statistics

`call-kimi`

Call Moonshot AI's Kimi API with a prompt.

Input:

prompt (string): The prompt to send to Kimi
model (optional, string): Kimi model to use (default: moonshot-v1-8k)
temperature (optional, number): Temperature for response randomness (0-2, default: 0.7)
max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

Kimi response with model information and token usage statistics

`call-perplexity`

Call Perplexity AI's API with a prompt.

Input:

prompt (string): The prompt to send to Perplexity
model (optional, string): Perplexity model to use (default: sonar-pro)
temperature (optional, number): Temperature for response randomness (0-2, default: 0.7)
max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

Perplexity response with model information and token usage statistics

`call-mistral`

Call Mistral AI's API with a prompt.

Input:

prompt (string): The prompt to send to Mistral
model (optional, string): Mistral model to use (default: mistral-large-latest)
temperature (optional, number): Temperature for response randomness (0-2, default: 0.7)
max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

Mistral response with model information and token usage statistics

🔄 Combined Tools

`call-all-llms`

Call all available LLM APIs (ChatGPT, Claude, DeepSeek, Gemini, Grok, Kimi, Perplexity, Mistral) with the same prompt and get combined responses.

Input:

prompt (string): The prompt to send to all LLMs
temperature (optional, number): Temperature for response randomness (0-2, default: 0.7)
max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

Combined responses from all LLMs with individual model information and usage statistics
Summary of successful responses and total tokens used

Example:

Multi-LLM Response Prompt: Explain quantum computing in simple terms --- ## CHATGPT Model: gpt-4 Quantum computing is like having a super-powered computer... --- ## CLAUDE Model: claude-3-sonnet-20240229 Quantum computing represents a fundamental shift... --- ## DEEPSEEK Model: deepseek-chat Quantum computing harnesses the principles of quantum mechanics... --- ## GEMINI Model: gemini-2.5-flash Quantum computing is a revolutionary approach to computation... --- Summary: - Successful responses: 4/4 - Total tokens used: 1650

`call-llm`

Call a specific LLM provider by name.

Input:

provider (string): The LLM provider to call ("chatgpt", "claude", "deepseek", or "gemini")
prompt (string): The prompt to send to the LLM
model (optional, string): Model to use (uses provider default if not specified)
temperature (optional, number): Temperature for response randomness (0-2, default: 0.7)
max_tokens (optional, number): Maximum tokens in response (default: 1000)

Output:

Response from the specified LLM with model information and usage statistics

⚙️ User Preferences & Model Tagging

The server includes a comprehensive user preference system that allows you to customize model selection based on question types and tags.

`get-user-preferences`

Get your current user preferences including default model, cost preference, and tag-based model preferences.

Input:

No parameters required

Output:

Current preferences including:
- Default model setting
- Cost preference (flagship vs cheaper)
- Tag-based preferences (which model to use for each tag)
- Available tags and model counts

Example:

{ "tool": "get-user-preferences", "arguments": {} }

`set-user-preferences`

Set user preferences for default model, cost preference, and tag-based model selection.

Input:

defaultModel (optional, string): Default model name (e.g., 'deepseek-r1', 'gpt-4o')
costPreference (optional, "flagship" | "cheaper"): Cost preference setting
tagPreferences (optional, object): Map tags to model names

Output:

Confirmation of updated preferences with model details

Example - Setting Tag-Based Preferences:

{ "tool": "set-user-preferences", "arguments": { "tagPreferences": { "coding": "deepseek-r1", "general": "gpt-4o", "business": "claude-3.5-sonnet-20241022" } } }

This allows you to automatically use DeepSeek R1 for coding questions, GPT-4o for general queries, and Claude 3.5 Sonnet for business-related tasks.

`get-models-by-tag`

Get all models that match a specific tag to help you choose the best model for your needs.

Input:

tag (string): One of: "coding", "business", "reasoning", "math", "creative", "general"

Output:

List of all models with the specified tag, grouped by provider, including:
- Model name
- Cost tier (flagship, standard, budget)
- All tags for the model
- Description

Example:

{ "tool": "get-models-by-tag", "arguments": { "tag": "coding" } }

Model Tags

Models are tagged based on their strengths and ideal use cases:

coding: Models optimized for programming, code generation, and software development
- Examples: deepseek-r1, deepseek-coder, gpt-4o, claude-3.5-sonnet-20241022
business: Models suited for business writing, analysis, and professional communication
- Examples: claude-3-opus-20240229, gpt-4o, gemini-1.5-pro
reasoning: Models with advanced reasoning capabilities for complex problem-solving
- Examples: deepseek-r1, o1-preview, claude-3.5-sonnet-20241022
math: Models specialized for mathematical problem-solving
- Examples: deepseek-r1, o1-preview, o1-mini
creative: Models optimized for creative writing, storytelling, and artistic content
- Examples: gpt-4o, claude-3-opus-20240229, gemini-1.5-pro
general: Versatile models for general-purpose tasks
- Examples: gpt-4o-mini, claude-3-haiku-20240307, gemini-1.5-flash

Preference Storage

User preferences are stored in:

Unix/macOS: ~/.cross-llm-mcp/preferences.json
Windows: %APPDATA%/cross-llm-mcp/preferences.json
Fallback: Project directory .cross-llm-mcp/preferences.json

Preferences can also be set via environment variables:

CROSS_LLM_DEFAULT_MODEL: Default model name
CROSS_LLM_COST_PREFERENCE: "flagship" or "cheaper"

Example: Setting Up Tag-Based Preferences

Here's a complete example of setting up preferences for different question types:

{ "tool": "set-user-preferences", "arguments": { "defaultModel": "gpt-4o", "costPreference": "cheaper", "tagPreferences": { "coding": "deepseek-r1", "general": "gpt-4o", "business": "claude-3.5-sonnet-20241022", "reasoning": "deepseek-r1", "math": "deepseek-r1", "creative": "gpt-4o" } } }

This configuration will:

Use DeepSeek R1 for coding, reasoning, and math questions
Use GPT-4o for general and creative queries
Use Claude 3.5 Sonnet for business-related tasks
Prefer cheaper models when multiple options are available

Related MCP server: URL Fetch MCP

Installation

Clone this repository:

git clone <repository-url> cd cross-llm-mcp

Install dependencies:

npm install

Build the project:

npm run build

Getting API Keys

OpenAI/ChatGPT

Visit OpenAI Platform
Sign up or log in to your account
Create a new API key
Add it to your .env file as OPENAI_API_KEY

Anthropic/Claude

Visit Anthropic Console
Sign up or log in to your account
Create a new API key
Add it to your .env file as ANTHROPIC_API_KEY

DeepSeek

Visit DeepSeek Platform
Sign up or log in to your account
Create a new API key
Add it to your .env file as DEEPSEEK_API_KEY

Google Gemini

Visit Google AI Studio
Sign up or log in to your Google account
Create a new API key
Add it to your Claude Desktop configuration as GEMINI_API_KEY

xAI/Grok

Visit xAI Platform
Sign up or log in to your account
Create a new API key
Add it to your Claude Desktop configuration as XAI_API_KEY

Moonshot AI/Kimi

Visit Moonshot AI Platform
Sign up or log in to your account
Create a new API key
Add it to your Claude Desktop configuration as KIMI_API_KEY

Perplexity AI

Visit the Perplexity AI Platform
Sign up or log in to your account
Generate a new API key from the developer console
Add it to your Claude Desktop configuration as PERPLEXITY_API_KEY

Mistral AI

Visit the Mistral AI Console
Sign up or log in to your account
Create a new API key
Add it to your Claude Desktop configuration as MISTRAL_API_KEY

Usage

Configuring Claude Desktop

Add the following configuration to your Claude Desktop MCP settings:

{ "cross-llm-mcp": { "command": "node", "args": ["/path/to/your/cross-llm-mcp/build/index.js"], "cwd": "/path/to/your/cross-llm-mcp", "env": { "OPENAI_API_KEY": "your_openai_api_key_here", "ANTHROPIC_API_KEY": "your_anthropic_api_key_here", "DEEPSEEK_API_KEY": "your_deepseek_api_key_here", "GEMINI_API_KEY": "your_gemini_api_key_here", "XAI_API_KEY": "your_grok_api_key_here", "KIMI_API_KEY": "your_kimi_api_key_here", "PERPLEXITY_API_KEY": "your_perplexity_api_key_here", "MISTRAL_API_KEY": "your_mistral_api_key_here" } } }

Replace the paths and API keys with your actual values:

Update the args path to point to your build/index.js file
Update the cwd path to your project directory
Add your actual API keys to the env section

Running the Server

The server runs automatically when configured in Claude Desktop. You can also run it manually:

npm start

The server runs on stdio and can be connected to any MCP-compatible client.

Example Queries

Here are some example queries you can make with this MCP server:

Call ChatGPT

{ "tool": "call-chatgpt", "arguments": { "prompt": "Explain quantum computing in simple terms", "temperature": 0.7, "max_tokens": 500 } }

Call Claude

{ "tool": "call-claude", "arguments": { "prompt": "What are the benefits of renewable energy?", "model": "claude-3-sonnet-20240229" } }

Call All LLMs

{ "tool": "call-all-llms", "arguments": { "prompt": "Write a short poem about artificial intelligence", "temperature": 0.8 } }

Call Specific LLM

{ "tool": "call-llm", "arguments": { "provider": "deepseek", "prompt": "Explain machine learning algorithms", "max_tokens": 800 } }

Call Gemini

{ "tool": "call-gemini", "arguments": { "prompt": "Write a creative story about AI", "model": "gemini-2.5-flash", "temperature": 0.9 } }

Call Grok

{ "tool": "call-grok", "arguments": { "prompt": "Tell me a joke about programming", "model": "grok-3", "temperature": 0.8 } }

Call Kimi

{ "tool": "call-kimi", "arguments": { "prompt": "Summarise the plot of The Matrix in two sentences", "model": "moonshot-v1-8k", "temperature": 0.7 } }

Call Perplexity

{ "tool": "call-perplexity", "arguments": { "prompt": "Summarize the latest AI research highlights in two paragraphs", "model": "sonar-medium-online", "temperature": 0.6 } }

Call Mistral

{ "tool": "call-mistral", "arguments": { "prompt": "Draft a concise product update for stakeholders", "model": "mistral-large-latest", "temperature": 0.7 } }

Use Cases

1. Multi-Perspective Analysis

Use call-all-llms to get different perspectives on the same topic from multiple AI models.

2. Model Comparison

Compare responses from different LLMs to understand their strengths and weaknesses.

3. Redundancy and Reliability

If one LLM is unavailable, you can still get responses from other providers.

4. Cost Optimization

Choose the most cost-effective LLM for your specific use case.

5. Quality Assurance

Cross-reference responses from multiple models to validate information.

6. Intelligent Model Selection

Use tag-based preferences to automatically select the best model for each question type. For example, use DeepSeek R1 for coding questions and GPT-4o for general queries.

Configuration

Claude Desktop Setup

The recommended way to use this MCP server is through Claude Desktop with environment variables configured directly in the MCP settings:

{ "cross-llm-mcp": { "command": "node", "args": [ "/Users/jamessangalli/Documents/projects/cross-llm-mcp/build/index.js" ], "cwd": "/Users/jamessangalli/Documents/projects/cross-llm-mcp", "env": { "OPENAI_API_KEY": "sk-proj-your-openai-key-here", "ANTHROPIC_API_KEY": "sk-ant-your-anthropic-key-here", "DEEPSEEK_API_KEY": "sk-your-deepseek-key-here", "GEMINI_API_KEY": "your-gemini-api-key-here" } } }

Environment Variables

The server reads the following environment variables:

OPENAI_API_KEY: Your OpenAI API key
ANTHROPIC_API_KEY: Your Anthropic API key
DEEPSEEK_API_KEY: Your DeepSeek API key
GEMINI_API_KEY: Your Google Gemini API key
XAI_API_KEY: Your xAI Grok API key
KIMI_API_KEY: Your Moonshot AI Kimi API key
PERPLEXITY_API_KEY: Your Perplexity AI API key
MISTRAL_API_KEY: Your Mistral AI API key
DEFAULT_CHATGPT_MODEL: Default ChatGPT model (default: gpt-4)
DEFAULT_CLAUDE_MODEL: Default Claude model (default: claude-3-sonnet-20240229)
DEFAULT_DEEPSEEK_MODEL: Default DeepSeek model (default: deepseek-chat)
DEFAULT_GEMINI_MODEL: Default Gemini model (default: gemini-2.5-flash)
DEFAULT_GROK_MODEL: Default Grok model (default: grok-3)
DEFAULT_KIMI_MODEL: Default Kimi model (default: moonshot-v1-8k)
DEFAULT_PERPLEXITY_MODEL: Default Perplexity model (default: sonar-pro)
DEFAULT_MISTRAL_MODEL: Default Mistral model (default: mistral-large-latest)
CROSS_LLM_DEFAULT_MODEL: User's default model preference
CROSS_LLM_COST_PREFERENCE: User's cost preference ("flagship" or "cheaper")

API Endpoints

This MCP server uses the following API endpoints:

OpenAI: https://api.openai.com/v1/chat/completions
Anthropic: https://api.anthropic.com/v1/messages
DeepSeek: https://api.deepseek.com/v1/chat/completions
Google Gemini: https://generativelanguage.googleapis.com/v1/models/{model}:generateContent
xAI Grok: https://api.x.ai/v1/chat/completions
Moonshot AI Kimi: https://api.moonshot.ai/v1/chat/completions
Perplexity AI: https://api.perplexity.ai/chat/completions
Mistral AI: https://api.mistral.ai/v1/chat/completions

Error Handling

The server includes comprehensive error handling with detailed messages:

Missing API Key

**ChatGPT Error:** OpenAI API key not configured

Invalid API Key

**Claude Error:** Claude API error: Invalid API key - please check your Anthropic API key

Rate Limiting

**DeepSeek Error:** DeepSeek API error: Rate limit exceeded - please try again later

Payment Issues

**ChatGPT Error:** ChatGPT API error: Payment required - please check your OpenAI billing

Network Issues

**Claude Error:** Claude API error: Network timeout

Supported Models

All models are tagged with their strengths (coding, business, reasoning, math, creative, general). Use the get-models-by-tag tool to find models optimized for specific use cases.

ChatGPT Models

gpt-4
gpt-4-turbo
gpt-3.5-turbo
And other OpenAI models

Claude Models

claude-3-sonnet-20240229
claude-3-opus-20240229
claude-3-haiku-20240307
And other Anthropic models

DeepSeek Models

deepseek-chat
deepseek-coder
And other DeepSeek models

Gemini Models

gemini-2.5-flash (default)
gemini-2.5-pro
gemini-2.0-flash
gemini-2.0-flash-001
And other Google Gemini models

Grok Models

grok-3 (default)
And other xAI Grok models

Kimi Models

moonshot-v1-8k (default)
moonshot-v1-32k
moonshot-v1-128k
And other Moonshot AI Kimi models

Perplexity Models

sonar-pro (default)
sonar-small-online
sonar-medium
And other Perplexity models

Mistral Models

mistral-large-latest (default)
mistral-small-latest
mixtral-8x7b-32768
And other Mistral models

Project Structure

cross-llm-mcp/ ├── src/ │ ├── index.ts # Main MCP server with all tools │ ├── types.ts # TypeScript type definitions │ ├── llm-clients.ts # LLM API client implementations │ ├── model-registry.ts # Model registry with tags and metadata │ └── preferences.ts # User preferences management ├── build/ # Compiled JavaScript output ├── env.example # Environment variables template ├── example-usage.md # Detailed usage examples ├── package.json # Project dependencies and scripts └── README.md # This file

Dependencies

@modelcontextprotocol/sdk - MCP SDK for server implementation
superagent - HTTP client for API requests
zod - Schema validation for tool parameters

Development

Building the Project

npm run build

Adding New LLM Providers

To add a new LLM provider:

Add the provider type to src/types.ts
Implement the client in src/llm-clients.ts
Add the tool to src/index.ts
Update the callAllLLMs method to include the new provider

Troubleshooting

Common Issues

Server won't start

Check that all dependencies are installed: npm install
Verify the build was successful: npm run build
Ensure the .env file exists and has valid API keys

API errors

Verify your API keys are correct and active
Check your API usage limits and billing status
Ensure you're using supported model names

No responses

Check that at least one API key is configured
Verify network connectivity
Look for error messages in the response

Debug Mode

For debugging, you can run the server directly:

node build/index.js

Donate

If you find this project useful, consider supporting it with Bitcoin:

⚡ Lightning Network

lnbc1pjhhsqepp5mjgwnvg0z53shm22hfe9us289lnaqkwv8rn2s0rtekg5vvj56xnqdqqcqzzsxqyz5vqsp5gu6vh9hyp94c7t3tkpqrp2r059t4vrw7ps78a4n0a2u52678c7yq9qyyssq7zcferywka50wcy75skjfrdrk930cuyx24rg55cwfuzxs49rc9c53mpz6zug5y2544pt8y9jflnq0ltlha26ed846jh0y7n4gm8jd3qqaautqa

₿ On-Chain

bc1ptzvr93pn959xq4et6sqzpfnkk2args22ewv5u2th4ps7hshfaqrshe0xtp

Ξ Ethereum / EVM Networks

0x42ea529282DDE0AA87B42d9E83316eb23FE62c3f

Donations from any EVM-compatible network (Ethereum, Polygon, Arbitrum, Optimism, BSC, Avalanche, etc.) will work perfectly! You can also send tokens like USDT, USDC, DAI, and other ERC-20 tokens to this address.