springboot-mcp-server

BillFarber/springboot-mcp-server

3.1

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A SpringBoot-based MCP server providing AI-powered tools and resources through a standardized protocol interface.

Tools
2
Resources
0
Prompts
0

SpringBoot MCP Server

A Model Context Protocol (MCP) server built with SpringBoot and SpringAI, providing AI-powered tools and resources through a standardized protocol interface.

Features

  • MCP Protocol Compliance: Full implementation of the Model Context Protocol specification
  • AI-Powered Tools: Text generation and data analysis using SpringAI
  • RESTful API: HTTP-based MCP communication
  • Extensible Architecture: Easy to add new tools and resources
  • Production Ready: Built with SpringBoot best practices

Available Tools

1. Text Generation (generate_text)

Generate text content using AI based on prompts.

Parameters:

  • prompt (string, required): The text prompt to generate content from
  • maxTokens (integer, optional): Maximum number of tokens to generate (default: 100)

2. Optic Code Generator (optic_code_generator)

Generate optic code snippets for data transformation - inspired by Rush's many talents.

Parameters:

  • schema (string, optional): The schema name to use in the optic code (default: "schema")
  • view (string, optional): The view name to use in the optic code (default: "view")

3. Optic Code Verifier (verify_optic_code)

Verify optic code for syntax and logical correctness (rebellious random verification).

Parameters:

  • optic_code (string, required): The optic code to verify for syntax and validity

4. MarkLogic Documentation Helper (marklogic_docs)

Help you out with MarkLogic documentation and guidance.

Parameters:

  • prompt (string, required): The user prompt describing what MarkLogic help you need

Available Resources

  • mcp://server/info: Server information and capabilities
  • mcp://tools/examples: Examples of how to use the available tools

Prerequisites

  • Java 17 or higher
  • Gradle 8.5 or higher

Getting Started

1. Clone and Build

./gradlew build

2. Configure AI (Optional)

To enable AI features, you have several options for configuring your Azure OpenAI credentials:

Option A: Using .env file (Recommended for development)

Create a .env file in the project root directory:

# Azure OpenAI Configuration - Keep this file secret!
AZURE_OPENAI_API_KEY=your-azure-openai-api-key
AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-35-turbo

Important: The .env file is automatically ignored by git and won't be committed to version control.

Option B: Using environment variables
export AZURE_OPENAI_API_KEY=your-azure-openai-api-key
export AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/
export AZURE_OPENAI_DEPLOYMENT_NAME=gpt-35-turbo
Option C: Direct configuration (Not recommended for production)

Set your credentials directly in src/main/resources/application.properties:

spring.ai.azure.openai.api-key=your-azure-openai-api-key
spring.ai.azure.openai.endpoint=https://your-resource-name.openai.azure.com/
spring.ai.azure.openai.chat.options.deployment-name=gpt-35-turbo
spring.ai.azure.openai.chat.options.model=gpt-3.5-turbo

3. Run the Server

./gradlew bootRun

The server will start on http://localhost:8080

4. Test the Server

Check server health:

curl -s http://localhost:8080/mcp/health | jq .

Get server capabilities:

curl -s http://localhost:8080/mcp/capabilities | jq .

Docker Deployment

The MCP server can be easily containerized and deployed using Docker. The build uses Spring Boot's Cloud Native Buildpacks for optimized, production-ready images.

1. Build Docker Image

Build the Docker image using Gradle's bootBuildImage task:

./gradlew clean bootBuildImage

This will create a Docker image named epic-mcp-server:latest (approximately 293MB) optimized for x86_64 architecture.

2. Start with Docker Compose

Start the containerized MCP server using Docker Compose:

docker compose up

Or run in the background:

docker compose up -d

The server will be available at http://localhost:8080

3. Test the Containerized Server

Once the container is running, test the MCP server with these curl commands:

Check Server Health
curl -s http://localhost:8080/mcp/health
List Available Tools
curl -H "Content-Type: application/json" \
     -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' \
     http://localhost:8080/mcp
Test Text Generation Tool
curl -H "Content-Type: application/json" \
     -d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"generate_text","arguments":{"prompt":"Write a haiku about Docker containers","maxTokens":50}}}' \
     http://localhost:8080/mcp
Test Optic Code Generator
curl -H "Content-Type: application/json" \
     -d '{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"optic_code_generator","arguments":{"prompt":"Create optic code to read user profiles from the database"}}}' \
     http://localhost:8080/mcp

4. Stop the Container

To stop the Docker container:

docker compose down

5. Alternative Docker Commands

You can also run the image directly with Docker:

# Run the container directly
docker run -p 8080:8080 epic-mcp-server:latest

# Run in the background
docker run -d -p 8080:8080 --name epic-mcp-server epic-mcp-server:latest

# Stop the container
docker stop epic-mcp-server
docker rm epic-mcp-server

Current Status

✅ MCP Server Working: The server implements the full MCP protocol and responds to all tool calls ✅ Mock AI Responses: Tools provide mock responses when AI is not configured ⚠️ Azure OpenAI Integration: Currently providing mock responses (SpringAI auto-configuration needs debugging)

Testing

The server is fully functional for MCP protocol testing. All tools work with mock responses. MCP Tool Calls

List Available Tools

curl -s -X POST http://localhost:8080/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": "1",
    "method": "tools/list",
    "params": {}
  }' | jq .

Test Text Generation Tool

curl -s -X POST http://localhost:8080/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": "2",
    "method": "tools/call",
    "params": {
      "name": "generate_text",
      "arguments": {
        "prompt": "Write a haiku about coding",
        "maxTokens": 50
      }
    }
  }' | jq .

List Available Resources

curl -s -X POST http://localhost:8080/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": "3",
    "method": "resources/list",
    "params": {}
  }' | jq .

Read a Resource

curl -s -X POST http://localhost:8080/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": "4",
    "method": "resources/read",
    "params": {
      "uri": "mcp://tools/examples"
    }
  }' | jq .

Initialize MCP Connection

curl -s -X POST http://localhost:8080/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": "0",
    "method": "initialize",
    "params": {}
  }' | jq .

MCP Client Integration

Initialize Connection

POST /mcp
{
  "jsonrpc": "2.0",
  "id": "1",
  "method": "initialize",
  "params": {}
}

List Available Tools

POST /mcp
{
  "jsonrpc": "2.0",
  "id": "2",
  "method": "tools/list",
  "params": {}
}

Call a Tool

POST /mcp
{
  "jsonrpc": "2.0",
  "id": "3",
  "method": "tools/call",
  "params": {
    "name": "generate_text",
    "arguments": {
      "prompt": "Write a haiku about programming",
      "maxTokens": 50
    }
  }
}

List Resources

POST /mcp
{
  "jsonrpc": "2.0",
  "id": "4",
  "method": "resources/list",
  "params": {}
}

Read a Resource

POST /mcp
{
  "jsonrpc": "2.0",
  "id": "5",
  "method": "resources/read",
  "params": {
    "uri": "mcp://server/info"
  }
}

Development

Adding New Tools

  1. Update the listTools() method in McpService to include your new tool
  2. Add a case for your tool in the callTool() method
  3. Implement the tool logic as a private method

Adding New Resources

  1. Update the listResources() method in McpService
  2. Add a case for your resource in the readResource() method
  3. Implement the resource reading logic

Running Tests

./gradlew test

Project Structure

src/
├── main/
│   ├── java/com/example/mcpserver/
│   │   ├── controller/          # REST API controllers
│   │   ├── model/              # MCP protocol models
│   │   ├── service/            # Business logic
│   │   └── McpServerApplication.java
│   └── resources/
│       └── application.properties
└── test/
    └── java/com/example/mcpserver/
        └── McpServerApplicationTests.java

Configuration

Key configuration options in application.properties:

  • server.port: Server port (default: 8080)
  • spring.ai.azure.openai.api-key: Azure OpenAI API key for AI features
  • spring.ai.azure.openai.endpoint: Azure OpenAI service endpoint
  • spring.ai.azure.openai.chat.options.deployment-name: Your deployment name
  • spring.ai.azure.openai.chat.options.model: AI model to use
  • Logging levels for debugging

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests for new functionality
  5. Submit a pull request

License

This project is licensed under the MIT License.

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