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OpenAI Complete MCP Server: Scalable Enterprise Text Generation - MCP Implementation

OpenAI Complete MCP Server: Scalable Enterprise Text Generation

The OpenAI Complete MCP Server delivers rapid, scalable text completion with enterprise-grade reliability, seamlessly powering AI-driven content creation and integration.

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About OpenAI Complete MCP Server

What is OpenAI Complete MCP Server: Scalable Enterprise Text Generation?

OpenAI Complete MCP Server is a middleware solution designed to bridge LLM clients with OpenAI-compatible APIs, enabling scalable text generation for enterprise workloads. Built around the Model Context Protocol (MCP), it focuses on serving base models by providing a streamlined "complete" tool for text completion tasks. Notably, it does not support chat-based interactions, instead prioritizing high-performance batch processing and stability for foundational language models.

How to Use OpenAI Complete MCP Server: Scalable Enterprise Text Generation?

Installation

git clone <repository-url>
cd mcp-openai-complete
pnpm install
pnpm run build

Configuration

Set required environment variables in your runtime environment:

OPENAI_API_KEY=your-hyperbolic-api-key
OPENAI_API_BASE=https://api.hyperbolic.xyz/v1
OPENAI_MODEL=meta-llama/Meta-Llama-3.1-405B

Running the Server

Start the service via command line:

pnpm start

Docker Deployment

docker build -t mcp-openai-complete .
docker run -it --rm \
  -e OPENAI_API_KEY="your-api-key" \
  -e OPENAI_MODEL="gpt-3.5-turbo-instruct" \
  mcp-openai-complete

Parameters for Completion Requests

  • prompt (required): Input text to generate completions from
  • max_tokens (optional, default 150): Output token limit
  • temperature (optional, default 0.7): Controls output randomness
  • top_p (optional, default 1.0): Nucleus sampling parameter
  • frequency_penalty (optional, default 0.0): Reduces repetitive patterns
  • presence_penalty (optional, default 0.0): Encourages topic diversity

OpenAI Complete MCP Server Features

Key Features of OpenAI Complete MCP Server: Scalable Enterprise Text Generation?

  • Single-purpose "complete" API endpoint for focused integration
  • Asynchronous processing architecture for non-blocking operations
  • Configurable timeouts with fail-safe fallback mechanisms
  • Request cancellation capability during active processing
  • Production-ready error handling and stability optimizations

Use Cases of OpenAI Complete MCP Server: Scalable Enterprise Text Generation?

Primary applications include:

  • Enterprise-scale text generation for content creation pipelines
  • Batch processing of large volumes of text completion requests
  • Middle-tier API orchestration for distributed LLM workloads
  • Custom model fine-tuning and evaluation environments
  • High-throughput scenarios requiring deterministic output

OpenAI Complete MCP Server FAQ

FAQ from OpenAI Complete MCP Server: Scalable Enterprise Text Generation?

Does this server support chat completions?
No, the service is strictly for base model text completion without conversational context handling.
How are concurrent requests managed?
Uses asynchronous processing patterns to handle multiple requests efficiently without thread blocking.
What happens if an API call times out?
Predefined graceful fallback logic is executed while maintaining request state tracking.
Can I modify the default parameters?
All completion parameters are configurable through the request payload, except required API credentials.
Is this suitable for production environments?
Yes, designed with enterprise-grade error handling, logging, and scalability features.

Content

OpenAI Complete MCP Server

An MCP (Model Context Protocol) server that provides a clean interface for LLMs to use text completion capabilities through the MCP protocol. This server acts as a bridge between an LLM client and any OpenAI's compatible API. The primary use case is for base models , as the server does not provide support for chat completions.

Features

  • Provides a single tool named "complete" for generating text completions
  • Properly handles asynchronous processing to avoid blocking
  • Implements timeout handling with graceful fallbacks
  • Supports cancellation of ongoing requests

Installation

# Clone the repository
git clone <repository-url>
cd mcp-openai-complete

# Install dependencies
pnpm install

# Build the project
pnpm run build

Configuration

The following environment variables are required:

OPENAI_API_KEY=your-hyperbolic-api-key
OPENAI_API_BASE=https://api.hyperbolic.xyz/v1
OPENAI_MODEL=meta-llama/Meta-Llama-3.1-405B

Usage

Start the server:

pnpm start

This will start the server on stdio, making it available for MCP clients to communicate with.

Docker Usage

Building the Docker Image

docker build -t mcp-openai-complete .

Running the Container

# Run with environment variables
docker run -it --rm \
  -e OPENAI_API_KEY="your-api-key" \
  -e OPENAI_MODEL="gpt-3.5-turbo-instruct" \
  mcp-openai-complete

You can also use a .env file:

# Run with .env file
docker run -it --rm \
  --env-file .env \
  mcp-openai-complete

Parameters for the "complete" tool

  • prompt (string, required): The text prompt to complete
  • max_tokens (integer, optional): Maximum tokens to generate, default: 150
  • temperature (number, optional): Controls randomness (0-1), default: 0.7
  • top_p (number, optional): Controls diversity via nucleus sampling, default: 1.0
  • frequency_penalty (number, optional): Decreases repetition of token sequences, default: 0.0
  • presence_penalty (number, optional): Increases likelihood of talking about new topics, default: 0.0

Development

For development with auto-reloading:

npm run dev

License

MIT

Related MCP Servers & Clients

mcp-openai-completetext-completion

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