Provides text generation capabilities using Google's Gemini models including Gemini Pro and Gemini Flash
Allows direct access to Meta's Llama models for text generation
Allows text generation using NVIDIA's AI models through their API
Enables text generation using locally hosted models via Ollama
Provides text generation with GPT models and image generation with DALL-E 2 and DALL-E 3 models
Enables text generation using Perplexity's Sonar models through their API
Provides access to Vercel AI (v0) models for text generation
Outsource MCP
An MCP (Model Context Protocol) server that enables AI applications to outsource tasks to various model providers through a unified interface.
Compatible with any AI tool that supports the Model Context Protocol, including Claude Desktop, Cline, and other MCP-enabled applications. Built with FastMCP for the MCP server implementation and Agno for AI agent capabilities.
Features
- 🤖 Multi-Provider Support: Access 20+ AI providers through a single interface
- 📝 Text Generation: Generate text using models from OpenAI, Anthropic, Google, and more
- 🎨 Image Generation: Create images using DALL-E 3 and DALL-E 2
- 🔧 Simple API: Consistent interface with just three parameters: provider, model, and prompt
- 🔑 Flexible Authentication: Only configure API keys for the providers you use
Configuration
Add the following configuration to your MCP client. Consult your MCP client's documentation for specific configuration details.
Note: The environment variables are optional. Only include the API keys for the providers you want to use.
Quick Start
Once installed and configured, you can use the tools in your MCP client:
- Generate text: Use the
outsource_text
tool with provider "openai", model "gpt-4o-mini", and prompt "Write a haiku about coding" - Generate images: Use the
outsource_image
tool with provider "openai", model "dall-e-3", and prompt "A futuristic city skyline at sunset"
Tools
outsource_text
Creates an Agno agent with a specified provider and model to generate text responses.
Arguments:
provider
: The provider name (e.g., "openai", "anthropic", "google", "groq", etc.)model
: The model name (e.g., "gpt-4o", "claude-3-5-sonnet-20241022", "gemini-2.0-flash-exp")prompt
: The text prompt to send to the model
outsource_image
Generates images using AI models.
Arguments:
provider
: The provider name (currently only "openai" is supported)model
: The model name ("dall-e-3" or "dall-e-2")prompt
: The image generation prompt
Returns the URL of the generated image.
Note: Image generation is currently only supported by OpenAI models (DALL-E 2 and DALL-E 3). Other providers only support text generation.
Supported Providers
The following providers are supported. Use the provider name (in parentheses) as the provider
argument:
Core Providers
- OpenAI (
openai
) - GPT-4, GPT-3.5, DALL-E, etc. | Models - Anthropic (
anthropic
) - Claude 3.5, Claude 3, etc. | Models - Google (
google
) - Gemini Pro, Gemini Flash, etc. | Models - Groq (
groq
) - Llama 3, Mixtral, etc. | Models - DeepSeek (
deepseek
) - DeepSeek Chat & Coder | Models - xAI (
xai
) - Grok models | Models - Perplexity (
perplexity
) - Sonar models | Models
Additional Providers
- Cohere (
cohere
) - Command models | Models - Mistral AI (
mistral
) - Mistral Large, Medium, Small | Models - NVIDIA (
nvidia
) - Various models | Models - HuggingFace (
huggingface
) - Open source models | Models - Ollama (
ollama
) - Local models | Models - Fireworks AI (
fireworks
) - Fast inference | Models - OpenRouter (
openrouter
) - Multi-provider access | Models - Together AI (
together
) - Open source models | Models - Cerebras (
cerebras
) - Fast inference | Models - DeepInfra (
deepinfra
) - Optimized models | Models - SambaNova (
sambanova
) - Enterprise models | Models
Enterprise Providers
- AWS Bedrock (
aws
orbedrock
) - AWS-hosted models | Models - Azure AI (
azure
) - Azure-hosted models | Models - IBM WatsonX (
ibm
orwatsonx
) - IBM models | Models - LiteLLM (
litellm
) - Universal interface | Models - Vercel v0 (
vercel
orv0
) - Vercel AI | Models - Meta Llama (
meta
) - Direct Meta access | Models
Environment Variables
Each provider requires its corresponding API key:
Provider | Environment Variable | Example |
---|---|---|
OpenAI | OPENAI_API_KEY | sk-... |
Anthropic | ANTHROPIC_API_KEY | sk-ant-... |
GOOGLE_API_KEY | AIza... | |
Groq | GROQ_API_KEY | gsk_... |
DeepSeek | DEEPSEEK_API_KEY | sk-... |
xAI | XAI_API_KEY | xai-... |
Perplexity | PERPLEXITY_API_KEY | pplx-... |
Cohere | COHERE_API_KEY | ... |
Fireworks | FIREWORKS_API_KEY | ... |
HuggingFace | HUGGINGFACE_API_KEY | hf_... |
Mistral | MISTRAL_API_KEY | ... |
NVIDIA | NVIDIA_API_KEY | nvapi-... |
Ollama | OLLAMA_HOST | http://localhost:11434 |
OpenRouter | OPENROUTER_API_KEY | ... |
Together | TOGETHER_API_KEY | ... |
Cerebras | CEREBRAS_API_KEY | ... |
DeepInfra | DEEPINFRA_API_KEY | ... |
SambaNova | SAMBANOVA_API_KEY | ... |
AWS Bedrock | AWS credentials | Via AWS CLI/SDK |
Azure AI | Azure credentials | Via Azure CLI/SDK |
IBM WatsonX | IBM_WATSONX_API_KEY | ... |
Meta Llama | LLAMA_API_KEY | ... |
Note: Only configure the API keys for providers you plan to use.
Examples
Text Generation
Image Generation
Development
Prerequisites
- Python 3.11 or higher
- uv package manager
Setup
Testing with MCP Inspector
The MCP Inspector allows you to test the server interactively:
Running Tests
The test suite includes integration tests that verify both text and image generation:
Note: Integration tests require API keys to be set in your environment.
Troubleshooting
Common Issues
- "Error: Unknown provider"
- Check that you're using a supported provider name from the list above
- Provider names are case-insensitive
- "Error: OpenAI API error"
- Verify your API key is correctly set in the environment variables
- Check that your API key has access to the requested model
- Ensure you have sufficient credits/quota
- "Error: No image was generated"
- This can happen if the image generation request fails
- Try a simpler prompt or different model (dall-e-2 vs dall-e-3)
- Environment variables not working
- Make sure to restart your MCP client after updating the configuration
- Verify the configuration file location for your specific MCP client
- Check that the environment variables are properly formatted in the configuration
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
An MCP server that enables AI applications to access 20+ model providers (including OpenAI, Anthropic, Google) through a unified interface for text and image generation.
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