oatpp-mcp MCP Server

What does “oatpp-mcp” MCP Server do?

The oatpp-mcp MCP Server is an implementation of Anthropic’s Model Context Protocol (MCP) for the Oat++ web framework. It acts as a bridge between AI assistants and external APIs or services, enabling seamless integration and interaction. By exposing Oat++ API controllers and resources through the MCP protocol, oatpp-mcp allows AI agents to perform tasks such as querying APIs, managing files, and leveraging server-side tools. This enhances development workflows by enabling large language models (LLMs) and clients to access and manipulate backend data, automate operations, and standardize interactions through reusable prompt templates and workflows. The server can be run over STDIO or HTTP SSE, making it flexible for different deployment environments.

List of Prompts

• CodeReview
  A prompt template designed for code review tasks, enabling LLMs to analyze and provide feedback on code snippets submitted by users.

List of Resources

• File
  Exposes file system operations as a resource, allowing clients and LLMs to read from and write to files on the server.

(No other resources are explicitly listed in the available documentation.)

List of Tools

• Logger
  A tool that provides logging capabilities, enabling LLMs and clients to record events or actions during interactions with the server.

(No other tools are explicitly listed in the available documentation.)

Use Cases of this MCP Server

• Code Review Automation
  Developers can submit code snippets for automated review, leveraging LLMs to receive instant feedback and suggestions, streamlining code quality assurance.
• API Querying
  The server can auto-generate tools from Oat++ API controllers, enabling AI assistants to interact directly with custom APIs for data retrieval or process automation.
• File Management
  Through the File resource, AI agents can read and write files on the server, supporting tasks like configuration updates, log retrieval, or data preprocessing.
• Logging and Monitoring
  Using the Logger tool, developers can keep track of AI-driven actions, monitor workflows, and debug issues more efficiently.
• LLM Workflow Standardization
  By exposing standard prompts and tools, teams can create consistent and reusable workflows for LLM-based automation and integration.

How to set it up

Windsurf

1. Ensure you have all prerequisites installed (Oat++, Node.js if required, and oatpp-mcp built/installed).
2. Locate your Windsurf configuration file (e.g., settings.json).
3. Add the oatpp-mcp server under the mcpServers object:

   {
     "mcpServers": {
       "oatpp-mcp": {
         "command": "oatpp-mcp",
         "args": []
       }
     }
   }
   

4. Save your configuration and restart Windsurf.
5. Verify the oatpp-mcp server is running and accessible.

Securing API Keys

{
  "mcpServers": {
    "oatpp-mcp": {
      "command": "oatpp-mcp",
      "env": {
        "API_KEY": "env:OATPP_API_KEY"
      },
      "inputs": {
        "api_key": "${API_KEY}"
      }
    }
  }
}

Claude

1. Install Oat++ and oatpp-mcp as per build instructions.
2. Open Claude’s MCP integration config.
3. Register the oatpp-mcp server with the following JSON:

   {
     "mcpServers": {
       "oatpp-mcp": {
         "command": "oatpp-mcp",
         "args": []
       }
     }
   }
   

4. Restart Claude.
5. Test connectivity to the oatpp-mcp MCP server.

Securing API Keys
Follow the same pattern as in Windsurf.

Cursor

1. Build and install oatpp-mcp.
2. Edit Cursor’s configuration file (refer to documentation for file location).
3. Add oatpp-mcp as an MCP server:

   {
     "mcpServers": {
       "oatpp-mcp": {
         "command": "oatpp-mcp",
         "args": []
       }
     }
   }
   

4. Save changes and restart Cursor.
5. Ensure the server is listed and accessible.

Securing API Keys
Same as above.

Cline

1. Ensure prerequisites (Oat++, oatpp-mcp) are installed.
2. Edit Cline’s MCP server configuration.
3. Add oatpp-mcp using:

   {
     "mcpServers": {
       "oatpp-mcp": {
         "command": "oatpp-mcp",
         "args": []
       }
     }
   }
   

4. Save and restart Cline.
5. Test the MCP server integration.

Securing API Keys
Same as above.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "oatpp-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “oatpp-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the documentation, oatpp-mcp provides a minimal but functional MCP server implementation, covering the protocol’s basics (prompts, resources, tools, and setup) but lacks evidence of advanced features like sampling or roots. The documentation is clear and covers the essentials but is limited in scope and detail.

MCP Score

Our opinion:
oatpp-mcp offers a clean, functional, and compliant MCP implementation for Oat++. While it covers the essentials (with at least one tool, prompt, and resource), it is not feature-rich and lacks documentation or evidence for roots, sampling, or a broader set of primitives. It is a good starting point for Oat++ users but may require extension for advanced workflows.

Rating:
6/10 – Good foundation and protocol compliance, but limited in feature exposure and extensibility based on available documentation.