MCP Servers to Connect to Different AI Models: OpenAI, Anthropic, Gemini, and More

MCP servers act as standardized protocol bridges that expose AI model APIs—including OpenAI, Anthropic, Google Gemini, and DeepSeek—through the Model Context Protocol interface, enabling agents to switch providers by changing configuration rather than rewriting code.

The punkpeye/awesome-mcp-servers repository maintains the authoritative registry of these implementations. According to the source code analysis of README.md, MCP servers translate generic JSON-RPC requests into provider-specific HTTP calls, allowing AI agents to discover and invoke tools without hard-coding SDK dependencies for each large language model provider.

How MCP Servers Bridge AI Model APIs

The Model Context Protocol defines a uniform HTTP interface based on three core operations: tools/list, tools/get, and tools/call. When connecting to different AI models, the architecture follows a strict translation pattern:

1. MCP Client sends a JSON-RPC request (e.g., tools/call with method chat/completions) to the MCP server.
2. MCP Server translates the generic request into the provider's native API format, injects authentication headers, and forwards the call.
3. Provider API returns the response, which the server restructures into the standardized MCP schema before returning to the client.

Because each bridge implements the same MCP contract, switching from OpenAI to Anthropic requires only changing the server URL or serverId in configuration—no code modifications are necessary.

Model-Specific MCP Server Bridges

Individual providers offer dedicated MCP servers that expose their native capabilities through the protocol.

OpenAI GPT Models

The jaspertvdm/mcp-server-openai-bridge provides direct access to GPT-4, GPT-4o, and embedding models. Install via:

npx -y @openai/mcp

Alternative implementations include pierrebrunelle/mcp-server-openai (npx -y @pierrebrunelle/openai-mcp) and mzxrai/mcp-openai (npx -y @mzxrai/mcp-openai), which offer lightweight wrappers with minimal dependencies. All three expose the chat/completions tool through the MCP interface as documented in the repository's README.md.

Anthropic Claude

While dedicated Anthropic bridges exist (such as jaspertvdm/mcp-server-anthropic-bridge), they are primarily distributed through aggregator servers like Correctover/mcp-server. These aggregators proxy Claude API calls alongside other providers, handling authentication and request formatting internally.

Google Gemini

The jaspertvdm/mcp-server-gemini-bridge exposes Gemini Pro and Flash models through a TypeScript implementation. Install via:

npx -y @gemini/mcp

DeepSeek and xAI Grok

For DeepSeek models, use arikusi/deepseek-mcp-server (npx -y @deepseek/mcp), which supports chat, vision, and image generation tools. For xAI's Grok, the merterbak/Grok-MCP package (pip install grok-mcp) provides an OpenAI-compatible bridge to the Grok API.

Multi-Model Aggregator MCP Servers

Aggregators allow a single MCP endpoint to route calls to multiple providers based on configuration, latency, or cost.

Correctover MCP Server

The Correctover/mcp-server (npx -y correctover-mcp-server) aggregates nine providers: OpenAI, Anthropic, DeepSeek, Moonshot, Zhipu AI, Qwen, SiliconFlow, Groq, and Together AI. It features automatic failover when providers experience latency spikes and can route requests based on cost optimization algorithms.

1mcp Agent

1mcp/agent (npx -y @1mcp/agent) runs multiple bridge servers simultaneously, offering unified service discovery across model providers. This implementation is ideal for agents requiring high availability across diverse model backends.

Universal Translation Layers

2s-io/sdk (npx -y @2sio/mcp) provides 180+ tools including model-agnostic LLM interfaces, while Work90210/APIFold (npx -y @work90210/apifold) converts any REST API (including custom LLM endpoints) into a hosted MCP server.

Alternative Deployment Models

Beyond traditional API-key-based bridges, MCP servers support decentralized and self-hosted architectures.

Pay-Per-Call (x402) Bridges

Servers like blockrunai/blockrun-mcp and forgemeshlabs/coinopai-mcp implement the x402 payment protocol. These expose 30+ models without requiring API keys, instead charging USDC micropayments per inference call. This model eliminates quota management while providing access to premium models.

Self-Hosted Universal Bridges

The liquid server (uvx --from 'liquid-api[mcp]' liquid-mcp) works with OpenAI, Gemini, Anthropic, or any provider supported by LiteLLM. Similarly, agentbodega offers 65+ pay-per-call tools without API keys. These implementations are documented in the repository's README.md and are suitable for organizations requiring data sovereignty.

Implementation Examples

Calling OpenAI Through an MCP Bridge

To invoke GPT-4o through the OpenAI bridge, send a JSON-RPC request to the MCP endpoint:

curl -X POST https://mcp.openai.example.com/mcp \
  -H "Content-Type: application/json" \
  -d '{
        "jsonrpc":"2.0",
        "method":"tools/call",
        "params":{
          "tool":"chat/completions",
          "arguments":{
            "model":"gpt-4o",
            "messages":[{"role":"user","content":"Explain MCP in 2 sentences"}]
          }
        },
        "id":1
      }'

Using an Aggregator for Automatic Model Selection

The Correctover aggregator selects the cheapest or fastest available model automatically:

# Start the server locally

npx -y correctover-mcp-server

# Call the generic chat tool

curl -X POST http://localhost:3000/mcp \
  -H "Content-Type: application/json" \
  -d '{
        "jsonrpc":"2.0",
        "method":"tools/call",
        "params":{
          "tool":"chat",
          "arguments":{
            "messages":[{"role":"user","content":"List the models you support"}]
          }
        },
        "id":42
      }'

Pay-Per-Call Inference Without API Keys

Blockrun's x402 implementation requires no API key setup:

npx -y blockrun-mcp

curl -X POST http://localhost:3000/mcp \
  -H "Content-Type: application/json" \
  -d '{
        "jsonrpc":"2.0",
        "method":"tools/call",
        "params":{
          "tool":"chat/completions",
          "arguments":{
            "model":"gpt-5",
            "messages":[{"role":"user","content":"Write a haiku about AI"}]
          }
        },
        "id":7
      }'

Self-Hosted Multi-Provider Access with Liquid

The Liquid bridge supports any LiteLLM-compatible provider:

uvx --from 'liquid-api[mcp]' liquid-mcp

curl -X POST http://localhost:8000/mcp \
  -H "Content-Type: application/json" \
  -d '{
        "jsonrpc":"2.0",
        "method":"tools/call",
        "params":{
          "tool":"chat/completions",
          "arguments":{
            "model":"gemini-1.5-flash",
            "messages":[{"role":"user","content":"Summarise MCP"}]
          }
        },
        "id":99
      }'

Summary

• MCP servers standardize access to AI models through a JSON-RPC interface, eliminating provider-specific SDK dependencies.
• Single-provider bridges like jaspertvdm/mcp-server-openai-bridge offer direct access to GPT-4o and embedding models via npx commands.
• Aggregators such as Correctover/mcp-server manage multiple providers (OpenAI, Anthropic, DeepSeek, etc.) with automatic failover and cost routing.
• Pay-per-call bridges using the x402 protocol allow usage of 30+ models without API keys, charging USDC per request.
• Self-hosted options like liquid provide universal access to any LiteLLM-supported provider, ensuring data remains within your infrastructure.

Frequently Asked Questions

What is the Model Context Protocol (MCP)?

The Model Context Protocol is a standardized HTTP interface that uses JSON-RPC to expose AI tools and model endpoints. As implemented in the punkpeye/awesome-mcp-servers registry, MCP defines three core operations—tools/list, tools/get, and tools/call—that allow AI agents to discover capabilities and invoke models without hard-coding provider SDKs.

How do I switch between OpenAI and Anthropic using MCP servers?

Switching providers requires only changing the server configuration, not your application code. Point your MCP client from an OpenAI bridge URL (e.g., jaspertvdm/mcp-server-openai-bridge) to an Anthropic-compatible endpoint (e.g., via the Correctover/mcp-server aggregator) by updating the serverId or base URL in your configuration file. The tools/call method signature remains identical across providers.

Do MCP servers store or train AI models?

No. MCP servers function as lightweight protocol translators or bridges. They do not store model weights, training data, or inference caches. According to the repository's architecture documentation, these servers only forward API calls to provider endpoints (OpenAI, Anthropic, etc.) and return responses, maintaining no persistent state about the models themselves.

Can I use MCP servers without managing API keys?

Yes. Several implementations eliminate traditional API key management. The x402-based bridges (such as blockrunai/blockrun-mcp) use USDC micropayments instead of keys, while aggregators like Correctover/mcp-server can manage authentication internally. Additionally, self-hosted bridges like liquid can proxy to internal endpoints that handle authentication at the infrastructure level.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}