OpenAI Agents MCP Server
# OpenAI Agents MCP Server
[](https://smithery.ai/server/@lroolle/openai-agents-mcp-server)
A Model Context Protocol (MCP) server that exposes OpenAI agents through the MCP protocol.
## Features
This server exposes both individual agents and a multi-agent orchestrator using the OpenAI Agents SDK:
### Individual Specialized Agents
- **Web Search Agent**: A specialized agent for searching the web for real-time information
- **File Search Agent**: A specialized agent for searching and analyzing files in OpenAI's vector store
- **Computer Action Agent**: A specialized agent for performing actions on your computer safely
### Multi-Agent Orchestrator
- **Orchestrator Agent**: A powerful agent that can coordinate between the specialized agents, choosing the right one(s) for each task
Each agent is accessed through the MCP protocol, making them available to any MCP client, including the Claude desktop app.
## Installation
### Prerequisites
- Python 3.11 or higher
- [uv](https://github.com/astral-sh/uv) package manager (recommended)
- OpenAI API key
### Installing via Smithery
To install openai-agents-mcp-server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@lroolle/openai-agents-mcp-server):
```bash
npx -y @smithery/cli install @lroolle/openai-agents-mcp-server --client claude
```
### Claude Desktop
```
"mcpServers": {
"openai-agents-mcp-server": {
"command": "uvx",
"args": ["openai-agents-mcp-server"],
"env": {
"OPENAI_API_KEY": "your-api-key-here"
}
}
}
```
## Implementation Details
### Tool Requirements
- **WebSearchTool**: No required parameters, but can accept optional location context
- **FileSearchTool**: Requires vector_store_ids (IDs from your OpenAI vector stores)
- **ComputerTool**: Requires an AsyncComputer implementation (currently simulated)
### Customization
You can customize this server by:
1. Implementing a full AsyncComputer interface to enable real computer interactions
2. Adding additional specialized agents for other OpenAI tools
3. Enhancing the orchestrator agent to handle more complex workflows
## Configuration
You can configure the server using environment variables:
- `OPENAI_API_KEY`: Your OpenAI API key (required)
- `MCP_TRANSPORT`: Transport protocol to use (default: "stdio", can be "sse")
## Development
### Setup development environment
```bash
# Clone the repository
git clone https://github.com/lroolle/openai-agents-mcp-server.git
cd openai-agents-mcp-server
# Create a virtual environment
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install dependencies
uv sync --dev
```
### Testing with MCP Inspector
You can test the server using the MCP Inspector:
```bash
# In one terminal, run the server with SSE transport
export OPENAI_API_KEY=your-api-key
export MCP_TRANSPORT=sse
uv run mcp dev src/agents_mcp_server/server.py
```
Then open a web browser and navigate to http://localhost:5173.
## License
MIT
TDQS
Scored across 4 tools
The tools have mostly distinct purposes with clear specialization: computer actions, file search, web search, and a multi-tool orchestrator. However, the multi_tool_agent overlaps with the specialized agents by potentially performing their functions, which could cause some confusion about when to use it versus the specific agents.
All tool names follow a consistent snake_case pattern with a clear '_agent' suffix (e.g., computer_action_agent, file_search_agent). The naming is predictable and uniform across all four tools, making them easy to identify and categorize.
With 4 tools, the count is reasonable for an agent orchestration server, covering core areas like computer actions, file search, web search, and multi-tool coordination. It is slightly thin but well-scoped, as each tool serves a distinct role without unnecessary bloat.
The tool set covers key agent functionalities (computer actions, file search, web search, and orchestration), but there are notable gaps. For example, there is no dedicated tool for database operations, API interactions, or other common agent tasks, which might limit coverage for broader use cases in the agent domain.