OpenAI

You run an MCP server that orchestrates OpenAI models and a set of specialized tools to analyze, optimize, and batch-process prompts. It enables token-efficient interactions, smart context management, and flexible model usage through a lightweight, transport-agnostic setup that you can integrate with Claude Code or other clients.

How to use

You interact with the MCP server through an MCP client or Claude Code integration. Use the available tools to perform enhanced model calls, analyze token usage, optimize context windows, and process multiple prompts in parallel. Start by configuring your environment, then invoke the tools that match your task: analysis, generation, reasoning, or coding.

Common usage patterns include analyzing code for security or performance, batching several prompts to improve throughput, and trimming long documents to fit within token limits without losing essential meaning. You can override models per call, tune reasoning depth, and adjust verbosity to balance detail against cost.

How to install

Prerequisites: you need Node.js installed on your system. You can install the MCP server globally or run it directly with npx.

# Global install
npm install -g openai-mcp-server

# Or use npx (no installation needed)
npx openai-mcp-server

Additional setup and configuration

Obtain your OpenAI API key from the OpenAI platform and export it as an environment variable to authorize requests.

export OPENAI_API_KEY="your-api-key-here"

Configuring Claude Code integration

Add the MCP server to Claude Code using a stdio transport. The following command runs the MCP server via npx and supplies your API key in the environment. Adapt the key to your environment.

claude mcp add --transport stdio openai-gpt5 \
  "OPENAI_API_KEY=your-key-here npx openai-mcp-server"

Available tools

call_gpt5

Enhanced GPT model calling with optimized system prompts and advanced reasoning. Supports domain, reasoningEffort, verbosity, and optional model override.

list_models

Lists available OpenAI models with capabilities and metadata. Can include detailed model information when requested.

analyze_token_usage

Analyzes text for token usage, provides token counts, cost estimation, and optimization suggestions to improve efficiency.

optimize_context_window

Optimizes long context to fit within token limits while preserving important content using various preservation strategies.

process_batch_prompts

Processes multiple prompts in parallel with concurrency control, automatic retry, and performance metrics.