LLM Bridge MCP allows AI agents to interact with multiple large language models through a standardized interface. It leverages the Message Control
LLM Bridge MCP allows AI agents to interact with multiple large language models through a standardized interface. It leverages the Message Control Protocol (MCP) to provide seamless access to different LLM providers, making it easy to. Exposed over MCP by the llm bridge mcp mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
Everything the assistant can do here goes through one of these:
prompt — The text prompt to send to the LLMmodel_name — Specific model to use (default: "openai:gpt-4o-mini")temperature — Controls randomness (0.0 to 1.0)max_tokens — Maximum number of tokens to generatesystem_prompt — Optional system prompt to guide the model's behaviorTroubleshooting — The Troubleshooting tool exposed by this serverYou will need 4 environment variables: OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, DEEPSEEK_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
The server ships on npm as @smithery/cli, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Llm Bridge MCP's toolset — prompt, model_name, temperature and 3 more — is a fair guide to whether it matches your workflow. It is maintained by sjquant; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| prompt | The text prompt to send to the LLM |
| model_name | Specific model to use (default: "openai:gpt-4o-mini") |
| temperature | Controls randomness (0.0 to 1.0) |
| max_tokens | Maximum number of tokens to generate |
| system_prompt | Optional system prompt to guide the model's behavior |
| Troubleshooting | The Troubleshooting tool exposed by this server. |
{
"mcpServers": {
"llm-bridge": {
"command": "npx",
"args": ["-y", "@smithery/cli"],
"env": {
"OPENAI_API_KEY": "your-value",
"ANTHROPIC_API_KEY": "your-value",
"GOOGLE_API_KEY": "your-value",
"DEEPSEEK_API_KEY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| ANTHROPIC_API_KEY | Credential the server authenticates with. | Yes |
| GOOGLE_API_KEY | Credential the server authenticates with. | Yes |
| DEEPSEEK_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.