Prompt Tester MCP Server: Universal Testing and Integration for AI Models

Overview: What is Prompt Tester MCP Server?

Prompt Tester is an MCP (Model Context Protocol) server designed to facilitate testing of AI prompts across different providers such as OpenAI and Anthropic. This server acts as a bridge, allowing developers to test and validate their text-based applications using various artificial intelligence models through a standardized protocol, enhancing the efficiency and flexibility in developing AI ecosystems.

🔧 Core Features & MCP Capabilities

Prompt Tester supports testing with both prominent providers: OpenAI and Anthropic. It allows configurations such as setting system prompts and user prompts, ensuring that the interaction between the model and its environment is tailored to specific use cases. The server then returns either formatted responses or error messages, providing clear feedback for developers. Additionally, it offers an easy setup process with support for both environment variables and a .env file for convenience.

⚙️ MCP Architecture & Protocol Implementation

Prompt Tester adheres strictly to the Model Context Protocol (MCP), ensuring seamless integration with various AI applications like Claude Desktop, Continue, Cursor, and more. The server is built using modern web technologies and supports two transport methods: standard input-output (stdio) and Server-Sent Events (SSE). By offering these transport options, developers can choose the most efficient method for their specific use cases.

MCP Protocol Flow Diagram

graph TD
    A[AI Application] -->|MCP Client| B[MPC Protocol]
    B -- Request and Response Messages --> C[MPC Server]
    C -- Data & Context --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

MCP Client Compatibility Matrix

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

🚀 Getting Started with Installation

Initializing and setting up the Prompt Tester MCP server is straightforward. You can install it either using pip or through uv. Below are detailed steps for both methods:

Option 1: Using pip

pip install -e .

Option 2: Using uv

uv install -e .

💡 Key Use Cases in AI Workflows

Prompt Tester is particularly useful in scenarios where developers need to test different AI models or integrate custom prompts. For instance, a content creator might use Prompt Tester to explore how various prompts impact the output of an AI-generated blog post before implementing it across multiple platforms.

Real-World Example 1: Content Creation

A marketing agency uses Prompt Tester to experiment with different system and user prompts for generating promotional copy. They can quickly switch between OpenAI's GPT-4 and Anthropic's Claude-3 models to find the best combination that aligns with their branding guidelines.

Real-World Example 2: Customer Support Automation

In a customer support setting, engineers may use Prompt Tester to test how AI-driven chatbots handle various user inputs. By fine-tuning both system and user prompts, they can customize responses more effectively, leading to better customer satisfaction and efficiency in troubleshooting.

🔌 Integration with MCP Clients

To integrate Prompt Tester with other applications using the Model Context Protocol, follow these steps:

1. Install the necessary mcp tools installed.
2. Configure API keys either through environment variables or a .env file as specified below.
3. Run the server using appropriate transport methods.

Setting up and running the server with specific parameters is shown in the example commands provided in the README.

📊 Performance & Compatibility Matrix

Prompt Tester ensures compatibility across various MCP clients, providing reliable performance for diverse testing needs. It supports OpenAI and Anthropic out of the box, ensuring seamless integration with both providers.

🛠️ Advanced Configuration & Security

For developers looking to modify or extend Prompt Tester's capabilities, advanced configurations can be made through environment variables:

export OPENAI_API_KEY=your-openai-api-key-here
export ANTHROPIC_API_KEY=your-anthropic-api-key-here

Alternatively, a .env file can contain these settings.

❓ Frequently Asked Questions (FAQ)

1. How do I integrate Prompt Tester with my application?

   • You can install the server via pip or directly from your development environment and configure API keys and commands to match your needs.

2. Can I test multiple providers simultaneously?

   • Yes, you can use different configurations for OpenAI and Anthropic by setting up separate API key environments.

3. How do I handle errors during testing?

   • The server returns formatted error messages which are easily parseable to help find issues quickly.

4. Is there a limit on the number of tokens generated per request?

   • You can set max_tokens in the parameters, allowing you to control how many tokens are returned.

5. How secure is Prompt Tester's data handling?

   • Ensure that API keys and other sensitive information are stored securely and not shared publicly.

👨‍💻 Development & Contribution Guidelines

Contributions to Prompt Tester are welcome. Developers can explore issues, review pull requests, or enhance the documentation. Detailed instructions for contributing can be found in the repository's CONTRIBUTING.md file.

🌐 MCP Ecosystem & Resources

For more information on Model Context Protocol and its applications, visit the official MCP documentation website. This site offers comprehensive guides and resources for developers looking to integrate MCP into their projects.

Configuration Code Sample

{
  "mcpServers": {
    "prompt-tester": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-prompt-tester"],
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key-here",
        "ANTHROPIC_API_KEY": "your-anthropic-api-key-here"
      }
    }
  }
}

By following these guidelines and integration processes, developers can effectively use Prompt Tester to enhance their AI workflows with robust testing capabilities.