Polybrain MCP Server
Enables AI agents to interact with OpenAI's language models through their API, supporting conversation history, model switching, and extended reasoning capabilities for compatible models.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Polybrain MCP Server ask GPT-4o to explain quantum computing basics"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Polybrain MCP Server
An MCP (Model Context Protocol) server for connecting AI agents to multiple LLM models. Supports conversation history, model switching, and seamless Claude Code integration.
Features
Multi-model support (OpenAI, OpenRouter, custom endpoints)
Conversation history management
Switch models mid-conversation
Extended thinking/reasoning support (configurable by provider)
Pure MCP protocol (silent by default)
Automatic server management
Related MCP server: Vox MCP
Installation
npm install -g polybrain
# or
pnpm add -g polybrainQuick Setup
1. Configure Models
Option A: YAML (recommended)
Create ~/.polybrain.yaml:
models:
- id: "gpt-4o"
modelName: "gpt-4o"
baseUrl: "https://api.openai.com/v1"
apiKey: "${OPENAI_API_KEY}"
provider: "openai"
- id: "gpt-5.1"
modelName: "openai/gpt-5.1"
baseUrl: "https://openrouter.io/api/v1"
apiKey: "${OPENROUTER_KEY}"
provider: "openrouter"Set env vars:
export OPENAI_API_KEY="sk-..."
export OPENROUTER_KEY="sk-or-..."Option B: Environment variables
export POLYBRAIN_BASE_URL="https://api.openai.com/v1"
export POLYBRAIN_API_KEY="sk-..."
export POLYBRAIN_MODEL_NAME="gpt-4o"2. Coding Agent Integration
Claude Code
Run the following command to add Polybrain to Claude Code:
claude mcp add -s user -t stdio polybrain -- polybrainOpenAI Codex
Open ~/.codex/config.toml and add:
[mcpServers.polybrain]
command = "polybrain"Usage
You can now ask your coding agent to consult specific models. For example:
"Ask deepseek what's the best way to install python on mac"
Or:
"What models are available from polybrain?"
Configuration Reference
Environment Variables
POLYBRAIN_BASE_URL- LLM API base URLPOLYBRAIN_API_KEY- API keyPOLYBRAIN_MODEL_NAME- Model namePOLYBRAIN_HTTP_PORT- Server port (default: 32701)POLYBRAIN_LOG_LEVEL- Log level (default: info)POLYBRAIN_DEBUG- Enable debug logging to stderrPOLYBRAIN_CONFIG_PATH- Custom config file path
YAML Config Fields
httpPort: 32701 # Optional
truncateLimit: 500 # Optional
logLevel: info # Optional
models: # Required
- id: "model-id" # Internal ID
modelName: "actual-model-name" # API model name
baseUrl: "https://api.url/v1" # API endpoint
apiKey: "key or ${ENV_VAR}" # API key
provider: "openai" # Optional: provider type for reasoning supportSupported Providers
The provider field enables provider-specific features like extended thinking/reasoning. If not specified, reasoning parameters will not be passed to the API (safe default).
Provider | Reasoning Support | Valid Values |
OpenAI | YES |
|
OpenRouter | VARIES |
|
Examples:
Use
provider: "openai"for OpenAI API models (GPT-4, o-series)Use
provider: "openrouter"for OpenRouter proxy service (supports 400+ models)Omit
providerfield if your endpoint doesn't support reasoning parameters
Example with reasoning:
models:
- id: "gpt-o1"
modelName: "o1"
baseUrl: "https://api.openai.com/v1"
apiKey: "${OPENAI_API_KEY}"
provider: "openai" # Enables reasoning support
- id: "gpt-5.1"
modelName: "openai/gpt-5.1"
baseUrl: "https://openrouter.io/api/v1"
apiKey: "${OPENROUTER_KEY}"
provider: "openrouter" # Enables reasoning supportTo use reasoning, set reasoning: true in the chat tool call. If the model and provider support it, you'll receive both the response and reasoning content.
Development
Setup
pnpm installBuild
pnpm buildLint & Format
pnpm lint
pnpm formatType Check
pnpm type-checkDevelopment Mode
pnpm devProject Structure
src/
├── bin/polybrain.ts # CLI entry point
├── launcher.ts # Server launcher & management
├── http-server.ts # HTTP server
├── index.ts # Main server logic
├── mcp-tools.ts # MCP tool definitions
├── conversation-manager.ts
├── openai-client.ts
├── config.ts
├── logger.ts
└── types.tsDebugging
Enable debug logs to stderr:
{
"mcpServers": {
"polybrain": {
"command": "polybrain",
"env": {
"POLYBRAIN_DEBUG": "true"
}
}
}
}Restart Server
After changing configuration in ~/.polybrain.yaml, restart the HTTP backend server:
polybrain --restartThis kills the background HTTP server. The next time you use polybrain, it will automatically start a fresh server with the updated configuration.
License
MIT
Available Tools
3 toolschatChat with Another LLM ModelA
Send a message to an available LLM for help, second opinions, or brainstorming; start new conversations, continue existing ones, or switch models mid-chat. In the first message you shall provide as much context as possible, since the model has no idea of the problem.
Example workflow:
chat(message: "hello", modelId: "gpt-5-mini") → conversationId: "abc1"
chat(message: "follow-up", conversationId: "abc1") → conversationId: "abc1" (continues)
chat(message: "same question", conversationId: "abc1", modelId: "deepseek-r1") → conversationId: "xyz9" (cloned with new model)
Inputs: message (required), conversationId, modelId, reasoning.
conversation_historyReview Your Conversation with Another ModelA
See what you've already discussed with a specific model. Useful for understanding context before continuing a conversation, reviewing advice you got, or checking previous responses. Long conversations are automatically shortened to save context.
Inputs: conversationId (required).
list_modelsSee Available Models to Talk ToA
Get all the models you can chat with. Each model has different strengths and expertise. Call this first to see which model is best for your question, or to find a specific model ID to use in the chat tool.
Inputs: no named parameters.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 3 tools
Each tool has a distinct, non-overlapping purpose: 'chat' handles message sending and conversation management, 'conversation_history' retrieves past discussions, and 'list_models' provides available model options. There is no ambiguity or confusion between their functions.
Two tools follow a clear verb_noun pattern ('list_models', 'conversation_history'), while 'chat' uses a simple noun. This minor deviation is readable and doesn't hinder understanding, but it breaks full consistency.
With 3 tools, the count is reasonable for a chat/LLM interaction server, covering core operations like messaging, history viewing, and model listing. It might benefit from additional tools (e.g., for conversation deletion), but it's well-scoped for its purpose.
The tools provide essential CRUD-like coverage for chat interactions: create/continue conversations, read history, and list models. A minor gap exists in operations like deleting conversations or managing conversation metadata, but agents can work around this.
Related MCP Connectors
Chat with any LLM, generate images, browse models and check your usage and costs.
AI routing, memory, guardrails, and governance. Routes across Claude, GPT, Gemini.
Real-time chat hub for AI agents — Claude Code, Cursor, Cline, Codex over MCP or REST.
Create and manage AI agents that collaborate and solve problems through natural language interacti…
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