## System Requirements

Before starting, ensure your system meets these requirements:

* Mac or Windows computer
* Latest Python version installed
* Latest version of `uv` installed

## Setting Up Your Environment

First, create a new Python project with `uv`:

```bash
# Create project directory
uv init mcp-client
cd mcp-client

# Create virtual environment
uv venv

# Activate virtual environment
# On Windows:
.venv\Scripts\activate
# On Unix or MacOS:
source .venv/bin/activate

# Install required packages
uv add mcp anthropic python-dotenv

# Remove boilerplate files
# On Windows:
del main.py
# On Unix or MacOS:
rm main.py

# Create our main file
touch client.py
```

## Setting Up Your API Key

You'll need an Anthropic API key from the [Anthropic Console](https://console.anthropic.com/settings/keys).

Create a `.env` file to store it:

```bash
# Create .env file
touch .env
```

Add your key to the `.env` file:

```bash
ANTHROPIC_API_KEY=<your key here>
```

Add `.env` to your `.gitignore`:

```bash
echo ".env" >> .gitignore
```

<Warning>
  Make sure you keep your `ANTHROPIC_API_KEY` secure!
</Warning>

## Creating the Client

### Basic Client Structure

First, let's set up our imports and create the basic client class:

```python
import asyncio
from typing import Optional
from contextlib import AsyncExitStack

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

from anthropic import Anthropic
from dotenv import load_dotenv

load_dotenv()  # load environment variables from .env

class MCPClient:
    def __init__(self):
        # Initialize session and client objects
        self.session: Optional[ClientSession] = None
        self.exit_stack = AsyncExitStack()
        self.anthropic = Anthropic()
    # methods will go here
```

### Server Connection Management

Next, we'll implement the method to connect to an MCP server:

```python
async def connect_to_server(self, server_script_path: str):
    """Connect to an MCP server

    Args:
        server_script_path: Path to the server script (.py or .js)
    """
    is_python = server_script_path.endswith('.py')
    is_js = server_script_path.endswith('.js')
    if not (is_python or is_js):
        raise ValueError("Server script must be a .py or .js file")

    command = "python" if is_python else "node"
    server_params = StdioServerParameters(
        command=command,
        args=[server_script_path],
        env=None
    )

    stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params))
    self.stdio, self.write = stdio_transport
    self.session = await self.exit_stack.enter_async_context(ClientSession(self.stdio, self.write))

    await self.session.initialize()

    # List available tools
    response = await self.session.list_tools()
    tools = response.tools
    print("\nConnected to server with tools:", [tool.name for tool in tools])
```

### Query Processing Logic

Now let's add the core functionality for processing queries and handling tool calls:

```python
async def process_query(self, query: str) -> str:
    """Process a query using Claude and available tools"""
    messages = [
        {
            "role": "user",
            "content": query
        }
    ]

    response = await self.session.list_tools()
    available_tools = [{
        "name": tool.name,
        "description": tool.description,
        "input_schema": tool.inputSchema
    } for tool in response.tools]

    # Initial Claude API call
    response = self.anthropic.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1000,
        messages=messages,
        tools=available_tools
    )

    # Process response and handle tool calls
    final_text = []

    assistant_message_content = []
    for content in response.content:
        if content.type == 'text':
            final_text.append(content.text)
            assistant_message_content.append(content)
        elif content.type == 'tool_use':
            tool_name = content.name
            tool_args = content.input

            # Execute tool call
            result = await self.session.call_tool(tool_name, tool_args)
            final_text.append(f"[Calling tool {tool_name} with args {tool_args}]")

            assistant_message_content.append(content)
            messages.append({
                "role": "assistant",
                "content": assistant_message_content
            })
            messages.append({
                "role": "user",
                "content": [
                    {
                        "type": "tool_result",
                        "tool_use_id": content.id,
                        "content": result.content
                    }
                ]
            })

            # Get next response from Claude
            response = self.anthropic.messages.create(
                model="claude-3-5-sonnet-20241022",
                max_tokens=1000,
                messages=messages,
                tools=available_tools
            )

            final_text.append(response.content[0].text)

    return "\n".join(final_text)
```

### Interactive Chat Interface

Now we'll add the chat loop and cleanup functionality:

```python
async def chat_loop(self):
    """Run an interactive chat loop"""
    print("\nMCP Client Started!")
    print("Type your queries or 'quit' to exit.")

    while True:
        try:
            query = input("\nQuery: ").strip()

            if query.lower() == 'quit':
                break

            response = await self.process_query(query)
            print("\n" + response)

        except Exception as e:
            print(f"\nError: {str(e)}")

async def cleanup(self):
    """Clean up resources"""
    await self.exit_stack.aclose()
```

### Main Entry Point

Finally, we'll add the main execution logic:

```python
async def main():
    if len(sys.argv) < 2:
        print("Usage: python client.py <path_to_server_script>")
        sys.exit(1)

    client = MCPClient()
    try:
        await client.connect_to_server(sys.argv[1])
        await client.chat_loop()
    finally:
        await client.cleanup()

if __name__ == "__main__":
    import sys
    asyncio.run(main())
```

You can find the complete `client.py` file [here.](https://gist.github.com/zckly/f3f28ea731e096e53b39b47bf0a2d4b1)

## Key Components Explained

### 1. Client Initialization

* The `MCPClient` class initializes with session management and API clients
* Uses `AsyncExitStack` for proper resource management
* Configures the Anthropic client for Claude interactions

### 2. Server Connection

* Supports both Python and Node.js servers
* Validates server script type
* Sets up proper communication channels
* Initializes the session and lists available tools

### 3. Query Processing

* Maintains conversation context
* Handles Claude's responses and tool calls
* Manages the message flow between Claude and tools
* Combines results into a coherent response

### 4. Interactive Interface

* Provides a simple command-line interface
* Handles user input and displays responses
* Includes basic error handling
* Allows graceful exit

### 5. Resource Management

* Proper cleanup of resources
* Error handling for connection issues
* Graceful shutdown procedures

## Common Customization Points

1. **Tool Handling**
   * Modify `process_query()` to handle specific tool types
   * Add custom error handling for tool calls
   * Implement tool-specific response formatting

2. **Response Processing**
   * Customize how tool results are formatted
   * Add response filtering or transformation
   * Implement custom logging

3. **User Interface**
   * Add a GUI or web interface
   * Implement rich console output
   * Add command history or auto-completion

## Running the Client

To run your client with any MCP server:

```bash
uv run client.py path/to/server.py # python server
uv run client.py path/to/build/index.js # node server
```

<Note>
  If you're continuing the weather tutorial from the server quickstart, your command might look something like this: `python client.py .../quickstart-resources/weather-server-python/weather.py`
</Note>

The client will:

1. Connect to the specified server
2. List available tools
3. Start an interactive chat session where you can:
   * Enter queries
   * See tool executions
   * Get responses from Claude

Here's an example of what it should look like if connected to the weather server from the server quickstart:

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/client-claude-cli-python.png" />
</Frame>

## How It Works

When you submit a query:

1. The client gets the list of available tools from the server
2. Your query is sent to Claude along with tool descriptions
3. Claude decides which tools (if any) to use
4. The client executes any requested tool calls through the server
5. Results are sent back to Claude
6. Claude provides a natural language response
7. The response is displayed to you

## Best practices

1. **Error Handling**
   * Always wrap tool calls in try-catch blocks
   * Provide meaningful error messages
   * Gracefully handle connection issues

2. **Resource Management**
   * Use `AsyncExitStack` for proper cleanup
   * Close connections when done
   * Handle server disconnections

3. **Security**
   * Store API keys securely in `.env`
   * Validate server responses
   * Be cautious with tool permissions

## Troubleshooting

### Server Path Issues

* Double-check the path to your server script is correct
* Use the absolute path if the relative path isn't working
* For Windows users, make sure to use forward slashes (/) or escaped backslashes (\\) in the path
* Verify the server file has the correct extension (.py for Python or .js for Node.js)

Example of correct path usage:

```bash
# Relative path
uv run client.py ./server/weather.py

# Absolute path
uv run client.py /Users/username/projects/mcp-server/weather.py

# Windows path (either format works)
uv run client.py C:/projects/mcp-server/weather.py
uv run client.py C:\\projects\\mcp-server\\weather.py
```

### Response Timing

* The first response might take up to 30 seconds to return
* This is normal and happens while:
  * The server initializes
  * Claude processes the query
  * Tools are being executed
* Subsequent responses are typically faster
* Don't interrupt the process during this initial waiting period

### Common Error Messages

If you see:

* `FileNotFoundError`: Check your server path
* `Connection refused`: Ensure the server is running and the path is correct
* `Tool execution failed`: Verify the tool's required environment variables are set
* `Timeout error`: Consider increasing the timeout in your client configuration

## System Requirements

Before starting, ensure your system meets these requirements:

* Mac or Windows computer
* Node.js 17 or higher installed
* Latest version of `npm` installed
* Anthropic API key (Claude)

## Setting Up Your Environment

First, let's create and set up our project:

<CodeGroup>
  ```bash MacOS/Linux
  # Create project directory
  mkdir mcp-client-typescript
  cd mcp-client-typescript

  # Initialize npm project
  npm init -y

  # Install dependencies
  npm install @anthropic-ai/sdk @modelcontextprotocol/sdk dotenv

  # Install dev dependencies
  npm install -D @types/node typescript

  # Create source file
  touch index.ts
  ```

  ```powershell Windows
  # Create project directory
  md mcp-client-typescript
  cd mcp-client-typescript

  # Initialize npm project
  npm init -y

  # Install dependencies
  npm install @anthropic-ai/sdk @modelcontextprotocol/sdk dotenv

  # Install dev dependencies
  npm install -D @types/node typescript

  # Create source file
  new-item index.ts
  ```
</CodeGroup>

Update your `package.json` to set `type: "module"` and a build script:

```json package.json
{
  "type": "module",
  "scripts": {
    "build": "tsc && chmod 755 build/index.js"
  }
}
```

Create a `tsconfig.json` in the root of your project:

```json tsconfig.json
{
  "compilerOptions": {
    "target": "ES2022",
    "module": "Node16",
    "moduleResolution": "Node16",
    "outDir": "./build",
    "rootDir": "./",
    "strict": true,
    "esModuleInterop": true,
    "skipLibCheck": true,
    "forceConsistentCasingInFileNames": true
  },
  "include": ["index.ts"],
  "exclude": ["node_modules"]
}
```

## Setting Up Your API Key

You'll need an Anthropic API key from the [Anthropic Console](https://console.anthropic.com/settings/keys).

Create a `.env` file to store it:

```bash
echo "ANTHROPIC_API_KEY=<your key here>" > .env
```

Add `.env` to your `.gitignore`:

```bash
echo ".env" >> .gitignore
```

<Warning>
  Make sure you keep your `ANTHROPIC_API_KEY` secure!
</Warning>

## Creating the Client

### Basic Client Structure

First, let's set up our imports and create the basic client class in `index.ts`:

```typescript
import { Anthropic } from "@anthropic-ai/sdk";
import {
  MessageParam,
  Tool,
} from "@anthropic-ai/sdk/resources/messages/messages.mjs";
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";
import readline from "readline/promises";
import dotenv from "dotenv";

dotenv.config();

const ANTHROPIC_API_KEY = process.env.ANTHROPIC_API_KEY;
if (!ANTHROPIC_API_KEY) {
  throw new Error("ANTHROPIC_API_KEY is not set");
}

class MCPClient {
  private mcp: Client;
  private anthropic: Anthropic;
  private transport: StdioClientTransport | null = null;
  private tools: Tool[] = [];

  constructor() {
    this.anthropic = new Anthropic({
      apiKey: ANTHROPIC_API_KEY,
    });
    this.mcp = new Client({ name: "mcp-client-cli", version: "1.0.0" });
  }
  // methods will go here
}
```

### Server Connection Management

Next, we'll implement the method to connect to an MCP server:

```typescript
async connectToServer(serverScriptPath: string) {
  try {
    const isJs = serverScriptPath.endsWith(".js");
    const isPy = serverScriptPath.endsWith(".py");
    if (!isJs && !isPy) {
      throw new Error("Server script must be a .js or .py file");
    }
    const command = isPy
      ? process.platform === "win32"
        ? "python"
        : "python3"
      : process.execPath;

    this.transport = new StdioClientTransport({
      command,
      args: [serverScriptPath],
    });
    this.mcp.connect(this.transport);

    const toolsResult = await this.mcp.listTools();
    this.tools = toolsResult.tools.map((tool) => {
      return {
        name: tool.name,
        description: tool.description,
        input_schema: tool.inputSchema,
      };
    });
    console.log(
      "Connected to server with tools:",
      this.tools.map(({ name }) => name)
    );
  } catch (e) {
    console.log("Failed to connect to MCP server: ", e);
    throw e;
  }
}
```

### Query Processing Logic

Now let's add the core functionality for processing queries and handling tool calls:

```typescript
async processQuery(query: string) {
  const messages: MessageParam[] = [
    {
      role: "user",
      content: query,
    },
  ];

  const response = await this.anthropic.messages.create({
    model: "claude-3-5-sonnet-20241022",
    max_tokens: 1000,
    messages,
    tools: this.tools,
  });

  const finalText = [];
  const toolResults = [];

  for (const content of response.content) {
    if (content.type === "text") {
      finalText.push(content.text);
    } else if (content.type === "tool_use") {
      const toolName = content.name;
      const toolArgs = content.input as { [x: string]: unknown } | undefined;

      const result = await this.mcp.callTool({
        name: toolName,
        arguments: toolArgs,
      });
      toolResults.push(result);
      finalText.push(
        `[Calling tool ${toolName} with args ${JSON.stringify(toolArgs)}]`
      );

      messages.push({
        role: "user",
        content: result.content as string,
      });

      const response = await this.anthropic.messages.create({
        model: "claude-3-5-sonnet-20241022",
        max_tokens: 1000,
        messages,
      });

      finalText.push(
        response.content[0].type === "text" ? response.content[0].text : ""
      );
    }
  }

  return finalText.join("\n");
}
```

### Interactive Chat Interface

Now we'll add the chat loop and cleanup functionality:

```typescript
async chatLoop() {
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
  });

  try {
    console.log("\nMCP Client Started!");
    console.log("Type your queries or 'quit' to exit.");

    while (true) {
      const message = await rl.question("\nQuery: ");
      if (message.toLowerCase() === "quit") {
        break;
      }
      const response = await this.processQuery(message);
      console.log("\n" + response);
    }
  } finally {
    rl.close();
  }
}

async cleanup() {
  await this.mcp.close();
}
```

### Main Entry Point

Finally, we'll add the main execution logic:

```typescript
async function main() {
  if (process.argv.length < 3) {
    console.log("Usage: node index.ts <path_to_server_script>");
    return;
  }
  const mcpClient = new MCPClient();
  try {
    await mcpClient.connectToServer(process.argv[2]);
    await mcpClient.chatLoop();
  } finally {
    await mcpClient.cleanup();
    process.exit(0);
  }
}

main();
```

## Running the Client

To run your client with any MCP server:

```bash
# Build TypeScript
npm run build

# Run the client
node build/index.js path/to/server.py # python server
node build/index.js path/to/build/index.js # node server
```

<Note>
  If you're continuing the weather tutorial from the server quickstart, your command might look something like this: `node build/index.js .../quickstart-resources/weather-server-typescript/build/index.js`
</Note>

**The client will:**

1. Connect to the specified server
2. List available tools
3. Start an interactive chat session where you can:
   * Enter queries
   * See tool executions
   * Get responses from Claude

## How It Works

When you submit a query:

1. The client gets the list of available tools from the server
2. Your query is sent to Claude along with tool descriptions
3. Claude decides which tools (if any) to use
4. The client executes any requested tool calls through the server
5. Results are sent back to Claude
6. Claude provides a natural language response
7. The response is displayed to you

## Best practices

1. **Error Handling**
   * Use TypeScript's type system for better error detection
   * Wrap tool calls in try-catch blocks
   * Provide meaningful error messages
   * Gracefully handle connection issues

2. **Security**
   * Store API keys securely in `.env`
   * Validate server responses
   * Be cautious with tool permissions

## Troubleshooting

### Server Path Issues

* Double-check the path to your server script is correct
* Use the absolute path if the relative path isn't working
* For Windows users, make sure to use forward slashes (/) or escaped backslashes (\\) in the path
* Verify the server file has the correct extension (.js for Node.js or .py for Python)

Example of correct path usage:

```bash
# Relative path
node build/index.js ./server/build/index.js

# Absolute path
node build/index.js /Users/username/projects/mcp-server/build/index.js

# Windows path (either format works)
node build/index.js C:/projects/mcp-server/build/index.js
node build/index.js C:\\projects\\mcp-server\\build\\index.js
```

### Response Timing

* The first response might take up to 30 seconds to return
* This is normal and happens while:
  * The server initializes
  * Claude processes the query
  * Tools are being executed
* Subsequent responses are typically faster
* Don't interrupt the process during this initial waiting period

### Common Error Messages

If you see:

* `Error: Cannot find module`: Check your build folder and ensure TypeScript compilation succeeded
* `Connection refused`: Ensure the server is running and the path is correct
* `Tool execution failed`: Verify the tool's required environment variables are set
* `ANTHROPIC_API_KEY is not set`: Check your .env file and environment variables
* `TypeError`: Ensure you're using the correct types for tool arguments

This example demonstrates how to build an interactive chatbot that combines Spring AI's Model Context Protocol (MCP) with the [Brave Search MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/brave-search). The application creates a conversational interface powered by Anthropic's Claude AI model that can perform internet searches through Brave Search, enabling natural language interactions with real-time web data.
[You can find the complete code for this tutorial here.](https://github.com/spring-projects/spring-ai-examples/tree/main/model-context-protocol/web-search/brave-chatbot)

## System Requirements

Before starting, ensure your system meets these requirements:

* Java 17 or higher
* Maven 3.6+
* npx package manager
* Anthropic API key (Claude)
* Brave Search API key

## Setting Up Your Environment

1. Install npx (Node Package eXecute):
   First, make sure to install [npm](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm)
   and then run:
   ```bash
   npm install -g npx
   ```

2. Clone the repository:
   ```bash
   git clone https://github.com/spring-projects/spring-ai-examples.git
   cd model-context-protocol/brave-chatbot
   ```

3. Set up your API keys:
   ```bash
   export ANTHROPIC_API_KEY='your-anthropic-api-key-here'
   export BRAVE_API_KEY='your-brave-api-key-here'
   ```

4. Build the application:
   ```bash
   ./mvnw clean install
   ```

5. Run the application using Maven:
   ```bash
   ./mvnw spring-boot:run
   ```

<Warning>
  Make sure you keep your `ANTHROPIC_API_KEY` and `BRAVE_API_KEY` keys secure!
</Warning>

## How it Works

The application integrates Spring AI with the Brave Search MCP server through several components:

### MCP Client Configuration

1. Required dependencies in pom.xml:

```xml
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-mcp-client</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-model-anthropic</artifactId>
</dependency>
```

2. Application properties (application.yml):

```yml
spring:
  ai:
    mcp:
      client:
        enabled: true
        name: brave-search-client
        version: 1.0.0
        type: SYNC
        request-timeout: 20s
        stdio:
          root-change-notification: true
          servers-configuration: classpath:/mcp-servers-config.json
        toolcallback:
          enabled: true
    anthropic:
      api-key: ${ANTHROPIC_API_KEY}
```

This activates the `spring-ai-starter-mcp-client` to create one or more `McpClient`s based on the provided server configuration.
The `spring.ai.mcp.client.toolcallback.enabled=true` property enables the tool callback mechanism, that automatically registers all MCP tool as spring ai tools.
It is disabled by default.

3. MCP Server Configuration (`mcp-servers-config.json`):

```json
{
  "mcpServers": {
    "brave-search": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-brave-search"
      ],
      "env": {
        "BRAVE_API_KEY": "<PUT YOUR BRAVE API KEY>"
      }
    }
  }
}
```

### Chat Implementation

The chatbot is implemented using Spring AI's ChatClient with MCP tool integration:

```java
var chatClient = chatClientBuilder
    .defaultSystem("You are useful assistant, expert in AI and Java.")
    .defaultToolCallbacks((Object[]) mcpToolAdapter.toolCallbacks())
    .defaultAdvisors(new MessageChatMemoryAdvisor(new InMemoryChatMemory()))
    .build();
```

<Warning>
  Breaking change: From SpringAI 1.0.0-M8 onwards, use `.defaultToolCallbacks(...)` instead of `.defaultTool(...)` to register MCP tools.
</Warning>

Key features:

* Uses Claude AI model for natural language understanding
* Integrates Brave Search through MCP for real-time web search capabilities
* Maintains conversation memory using InMemoryChatMemory
* Runs as an interactive command-line application

### Build and run

```bash
./mvnw clean install
java -jar ./target/ai-mcp-brave-chatbot-0.0.1-SNAPSHOT.jar
```

or

```bash
./mvnw spring-boot:run
```

The application will start an interactive chat session where you can ask questions. The chatbot will use Brave Search when it needs to find information from the internet to answer your queries.

The chatbot can:

* Answer questions using its built-in knowledge
* Perform web searches when needed using Brave Search
* Remember context from previous messages in the conversation
* Combine information from multiple sources to provide comprehensive answers

### Advanced Configuration

The MCP client supports additional configuration options:

* Client customization through `McpSyncClientCustomizer` or `McpAsyncClientCustomizer`
* Multiple clients with multiple transport types: `STDIO` and `SSE` (Server-Sent Events)
* Integration with Spring AI's tool execution framework
* Automatic client initialization and lifecycle management

For WebFlux-based applications, you can use the WebFlux starter instead:

```xml
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-mcp-client-webflux-spring-boot-starter</artifactId>
</dependency>
```

This provides similar functionality but uses a WebFlux-based SSE transport implementation, recommended for production deployments.

## System Requirements

Before starting, ensure your system meets these requirements:

* Java 17 or higher
* Anthropic API key (Claude)

## Setting up your environment

First, let's install `java` and `gradle` if you haven't already.
You can download `java` from [official Oracle JDK website](https://www.oracle.com/java/technologies/downloads/).
Verify your `java` installation:

```bash
java --version
```

Now, let's create and set up your project:

<CodeGroup>
  ```bash MacOS/Linux
  # Create a new directory for our project
  mkdir kotlin-mcp-client
  cd kotlin-mcp-client

  # Initialize a new kotlin project
  gradle init
  ```

  ```powershell Windows
  # Create a new directory for our project
  md kotlin-mcp-client
  cd kotlin-mcp-client
  # Initialize a new kotlin project
  gradle init
  ```
</CodeGroup>

After running `gradle init`, you will be presented with options for creating your project.
Select **Application** as the project type, **Kotlin** as the programming language, and **Java 17** as the Java version.

Alternatively, you can create a Kotlin application using the [IntelliJ IDEA project wizard](https://kotlinlang.org/docs/jvm-get-started.html).

After creating the project, add the following dependencies:

<CodeGroup>
  ```kotlin build.gradle.kts
  val mcpVersion = "0.4.0"
  val slf4jVersion = "2.0.9"
  val anthropicVersion = "0.8.0"

  dependencies {
      implementation("io.modelcontextprotocol:kotlin-sdk:$mcpVersion")
      implementation("org.slf4j:slf4j-nop:$slf4jVersion")
      implementation("com.anthropic:anthropic-java:$anthropicVersion")
  }
  ```

  ```groovy build.gradle
  def mcpVersion = '0.3.0'
  def slf4jVersion = '2.0.9'
  def anthropicVersion = '0.8.0'
  dependencies {
      implementation "io.modelcontextprotocol:kotlin-sdk:$mcpVersion"
      implementation "org.slf4j:slf4j-nop:$slf4jVersion"
      implementation "com.anthropic:anthropic-java:$anthropicVersion"
  }
  ```
</CodeGroup>

Also, add the following plugins to your build script:

<CodeGroup>
  ```kotlin build.gradle.kts
  plugins {
      id("com.github.johnrengelman.shadow") version "8.1.1"
  }
  ```

  ```groovy build.gradle
  plugins {
      id 'com.github.johnrengelman.shadow' version '8.1.1'
  }
  ```
</CodeGroup>

## Setting up your API key

You'll need an Anthropic API key from the [Anthropic Console](https://console.anthropic.com/settings/keys).

Set up your API key:

```bash
export ANTHROPIC_API_KEY='your-anthropic-api-key-here'
```

<Warning>
  Make sure your keep your `ANTHROPIC_API_KEY` secure!
</Warning>

## Creating the Client

### Basic Client Structure

First, let's create the basic client class:

```kotlin
class MCPClient : AutoCloseable {
    private val anthropic = AnthropicOkHttpClient.fromEnv()
    private val mcp: Client = Client(clientInfo = Implementation(name = "mcp-client-cli", version = "1.0.0"))
    private lateinit var tools: List<ToolUnion>

    // methods will go here

    override fun close() {
        runBlocking {
            mcp.close()
            anthropic.close()
        }
    }
```

### Server connection management

Next, we'll implement the method to connect to an MCP server:

```kotlin
suspend fun connectToServer(serverScriptPath: String) {
    try {
        val command = buildList {
            when (serverScriptPath.substringAfterLast(".")) {
                "js" -> add("node")
                "py" -> add(if (System.getProperty("os.name").lowercase().contains("win")) "python" else "python3")
                "jar" -> addAll(listOf("java", "-jar"))
                else -> throw IllegalArgumentException("Server script must be a .js, .py or .jar file")
            }
            add(serverScriptPath)
        }

        val process = ProcessBuilder(command).start()
        val transport = StdioClientTransport(
            input = process.inputStream.asSource().buffered(),
            output = process.outputStream.asSink().buffered()
        )

        mcp.connect(transport)

        val toolsResult = mcp.listTools()
        tools = toolsResult?.tools?.map { tool ->
            ToolUnion.ofTool(
                Tool.builder()
                    .name(tool.name)
                    .description(tool.description ?: "")
                    .inputSchema(
                        Tool.InputSchema.builder()
                            .type(JsonValue.from(tool.inputSchema.type))
                            .properties(tool.inputSchema.properties.toJsonValue())
                            .putAdditionalProperty("required", JsonValue.from(tool.inputSchema.required))
                            .build()
                    )
                    .build()
            )
        } ?: emptyList()
        println("Connected to server with tools: ${tools.joinToString(", ") { it.tool().get().name() }}")
    } catch (e: Exception) {
        println("Failed to connect to MCP server: $e")
        throw e
    }
}
```

Also create a helper function to convert from `JsonObject` to `JsonValue` for Anthropic:

```kotlin
private fun JsonObject.toJsonValue(): JsonValue {
    val mapper = ObjectMapper()
    val node = mapper.readTree(this.toString())
    return JsonValue.fromJsonNode(node)
}
```

### Query processing logic

Now let's add the core functionality for processing queries and handling tool calls:

```kotlin
private val messageParamsBuilder: MessageCreateParams.Builder = MessageCreateParams.builder()
    .model(Model.CLAUDE_3_5_SONNET_20241022)
    .maxTokens(1024)

suspend fun processQuery(query: String): String {
    val messages = mutableListOf(
        MessageParam.builder()
            .role(MessageParam.Role.USER)
            .content(query)
            .build()
    )

    val response = anthropic.messages().create(
        messageParamsBuilder
            .messages(messages)
            .tools(tools)
            .build()
    )

    val finalText = mutableListOf<String>()
    response.content().forEach { content ->
        when {
            content.isText() -> finalText.add(content.text().getOrNull()?.text() ?: "")

            content.isToolUse() -> {
                val toolName = content.toolUse().get().name()
                val toolArgs =
                    content.toolUse().get()._input().convert(object : TypeReference<Map<String, JsonValue>>() {})

                val result = mcp.callTool(
                    name = toolName,
                    arguments = toolArgs ?: emptyMap()
                )
                finalText.add("[Calling tool $toolName with args $toolArgs]")

                messages.add(
                    MessageParam.builder()
                        .role(MessageParam.Role.USER)
                        .content(
                            """
                                "type": "tool_result",
                                "tool_name": $toolName,
                                "result": ${result?.content?.joinToString("\n") { (it as TextContent).text ?: "" }}
                            """.trimIndent()
                        )
                        .build()
                )

                val aiResponse = anthropic.messages().create(
                    messageParamsBuilder
                        .messages(messages)
                        .build()
                )

                finalText.add(aiResponse.content().first().text().getOrNull()?.text() ?: "")
            }
        }
    }

    return finalText.joinToString("\n", prefix = "", postfix = "")
}
```

### Interactive chat

We'll add the chat loop:

```kotlin
suspend fun chatLoop() {
    println("\nMCP Client Started!")
    println("Type your queries or 'quit' to exit.")

    while (true) {
        print("\nQuery: ")
        val message = readLine() ?: break
        if (message.lowercase() == "quit") break
        val response = processQuery(message)
        println("\n$response")
    }
}
```

### Main entry point

Finally, we'll add the main execution function:

```kotlin
fun main(args: Array<String>) = runBlocking {
    if (args.isEmpty()) throw IllegalArgumentException("Usage: java -jar <your_path>/build/libs/kotlin-mcp-client-0.1.0-all.jar <path_to_server_script>")
    val serverPath = args.first()
    val client = MCPClient()
    client.use {
        client.connectToServer(serverPath)
        client.chatLoop()
    }
}
```

## Running the client

To run your client with any MCP server:

```bash
./gradlew build

# Run the client
java -jar build/libs/<your-jar-name>.jar path/to/server.jar # jvm server
java -jar build/libs/<your-jar-name>.jar path/to/server.py # python server
java -jar build/libs/<your-jar-name>.jar path/to/build/index.js # node server
```

<Note>
  If you're continuing the weather tutorial from the server quickstart, your command might look something like this: `java -jar build/libs/kotlin-mcp-client-0.1.0-all.jar .../samples/weather-stdio-server/build/libs/weather-stdio-server-0.1.0-all.jar`
</Note>

**The client will:**

1. Connect to the specified server
2. List available tools
3. Start an interactive chat session where you can:
   * Enter queries
   * See tool executions
   * Get responses from Claude

## How it works

Here's a high-level workflow schema:

```mermaid
---
config:
    theme: neutral
---
sequenceDiagram
    actor User
    participant Client
    participant Claude
    participant MCP_Server as MCP Server
    participant Tools

    User->>Client: Send query
    Client<<->>MCP_Server: Get available tools
    Client->>Claude: Send query with tool descriptions
    Claude-->>Client: Decide tool execution
    Client->>MCP_Server: Request tool execution
    MCP_Server->>Tools: Execute chosen tools
    Tools-->>MCP_Server: Return results
    MCP_Server-->>Client: Send results
    Client->>Claude: Send tool results
    Claude-->>Client: Provide final response
    Client-->>User: Display response
```

When you submit a query:

1. The client gets the list of available tools from the server
2. Your query is sent to Claude along with tool descriptions
3. Claude decides which tools (if any) to use
4. The client executes any requested tool calls through the server
5. Results are sent back to Claude
6. Claude provides a natural language response
7. The response is displayed to you

## Best practices

1. **Error Handling**
   * Leverage Kotlin's type system to model errors explicitly
   * Wrap external tool and API calls in `try-catch` blocks when exceptions are possible
   * Provide clear and meaningful error messages
   * Handle network timeouts and connection issues gracefully

2. **Security**
   * Store API keys and secrets securely in `local.properties`, environment variables, or secret managers
   * Validate all external responses to avoid unexpected or unsafe data usage
   * Be cautious with permissions and trust boundaries when using tools

## Troubleshooting

### Server Path Issues

* Double-check the path to your server script is correct
* Use the absolute path if the relative path isn't working
* For Windows users, make sure to use forward slashes (/) or escaped backslashes (\\) in the path
* Make sure that the required runtime is installed (java for Java, npm for Node.js, or uv for Python)
* Verify the server file has the correct extension (.jar for Java, .js for Node.js or .py for Python)

Example of correct path usage:

```bash
# Relative path
java -jar build/libs/client.jar ./server/build/libs/server.jar

# Absolute path
java -jar build/libs/client.jar /Users/username/projects/mcp-server/build/libs/server.jar

# Windows path (either format works)
java -jar build/libs/client.jar C:/projects/mcp-server/build/libs/server.jar
java -jar build/libs/client.jar C:\\projects\\mcp-server\\build\\libs\\server.jar
```

### Response Timing

* The first response might take up to 30 seconds to return
* This is normal and happens while:
  * The server initializes
  * Claude processes the query
  * Tools are being executed
* Subsequent responses are typically faster
* Don't interrupt the process during this initial waiting period

### Common Error Messages

If you see:

* `Connection refused`: Ensure the server is running and the path is correct
* `Tool execution failed`: Verify the tool's required environment variables are set
* `ANTHROPIC_API_KEY is not set`: Check your environment variables

## System Requirements

Before starting, ensure your system meets these requirements:

* .NET 8.0 or higher
* Anthropic API key (Claude)
* Windows, Linux, or MacOS

## Setting up your environment

First, create a new .NET project:

```bash
dotnet new console -n QuickstartClient
cd QuickstartClient
```

Then, add the required dependencies to your project:

```bash
dotnet add package ModelContextProtocol --prerelease
dotnet add package Anthropic.SDK
dotnet add package Microsoft.Extensions.Hosting
```

## Setting up your API key

You'll need an Anthropic API key from the [Anthropic Console](https://console.anthropic.com/settings/keys).

```bash
dotnet user-secrets init
dotnet user-secrets set "ANTHROPIC_API_KEY" "<your key here>"
```

## Creating the Client

### Basic Client Structure

First, let's setup the basic client class in the file `Program.cs`:

```csharp
using Anthropic.SDK;
using Microsoft.Extensions.AI;
using Microsoft.Extensions.Configuration;
using Microsoft.Extensions.Hosting;
using ModelContextProtocol.Client;
using ModelContextProtocol.Protocol.Transport;

var builder = Host.CreateApplicationBuilder(args);

builder.Configuration
    .AddEnvironmentVariables()
    .AddUserSecrets<Program>();
```

This creates the beginnings of a .NET console application that can read the API key from user secrets.

Next, we'll setup the MCP Client:

```csharp
var (command, arguments) = GetCommandAndArguments(args);

var clientTransport = new StdioClientTransport(new()
{
    Name = "Demo Server",
    Command = command,
    Arguments = arguments,
});

await using var mcpClient = await McpClientFactory.CreateAsync(clientTransport);

var tools = await mcpClient.ListToolsAsync();
foreach (var tool in tools)
{
    Console.WriteLine($"Connected to server with tools: {tool.Name}");
}
```

Add this function at the end of the `Program.cs` file:

```csharp
static (string command, string[] arguments) GetCommandAndArguments(string[] args)
{
    return args switch
    {
        [var script] when script.EndsWith(".py") => ("python", args),
        [var script] when script.EndsWith(".js") => ("node", args),
        [var script] when Directory.Exists(script) || (File.Exists(script) && script.EndsWith(".csproj")) => ("dotnet", ["run", "--project", script, "--no-build"]),
        _ => throw new NotSupportedException("An unsupported server script was provided. Supported scripts are .py, .js, or .csproj")
    };
}
```

This creates a MCP client that will connect to a server that is provided as a command line argument. It then lists the available tools from the connected server.

### Query processing logic

Now let's add the core functionality for processing queries and handling tool calls:

```csharp
using var anthropicClient = new AnthropicClient(new APIAuthentication(builder.Configuration["ANTHROPIC_API_KEY"]))
    .Messages
    .AsBuilder()
    .UseFunctionInvocation()
    .Build();

var options = new ChatOptions
{
    MaxOutputTokens = 1000,
    ModelId = "claude-3-5-sonnet-20241022",
    Tools = [.. tools]
};

Console.ForegroundColor = ConsoleColor.Green;
Console.WriteLine("MCP Client Started!");
Console.ResetColor();

PromptForInput();
while(Console.ReadLine() is string query && !"exit".Equals(query, StringComparison.OrdinalIgnoreCase))
{
    if (string.IsNullOrWhiteSpace(query))
    {
        PromptForInput();
        continue;
    }

    await foreach (var message in anthropicClient.GetStreamingResponseAsync(query, options))
    {
        Console.Write(message);
    }
    Console.WriteLine();

    PromptForInput();
}

static void PromptForInput()
{
    Console.WriteLine("Enter a command (or 'exit' to quit):");
    Console.ForegroundColor = ConsoleColor.Cyan;
    Console.Write("> ");
    Console.ResetColor();
}
```

## Key Components Explained

### 1. Client Initialization

* The client is initialized using `McpClientFactory.CreateAsync()`, which sets up the transport type and command to run the server.

### 2. Server Connection

* Supports Python, Node.js, and .NET servers.
* The server is started using the command specified in the arguments.
* Configures to use stdio for communication with the server.
* Initializes the session and available tools.

### 3. Query Processing

* Leverages [Microsoft.Extensions.AI](https://learn.microsoft.com/dotnet/ai/ai-extensions) for the chat client.
* Configures the `IChatClient` to use automatic tool (function) invocation.
* The client reads user input and sends it to the server.
* The server processes the query and returns a response.
* The response is displayed to the user.

## Running the Client

To run your client with any MCP server:

```bash
dotnet run -- path/to/server.csproj # dotnet server
dotnet run -- path/to/server.py # python server
dotnet run -- path/to/server.js # node server
```

<Note>
  If you're continuing the weather tutorial from the server quickstart, your command might look something like this: `dotnet run -- path/to/QuickstartWeatherServer`.
</Note>

The client will:

1. Connect to the specified server
2. List available tools
3. Start an interactive chat session where you can:
   * Enter queries
   * See tool executions
   * Get responses from Claude
4. Exit the session when done

Here's an example of what it should look like it connected to a weather server quickstart:

<Frame>
  <img src="https://mintlify.s3.us-west-1.amazonaws.com/mcp/images/quickstart-dotnet-client.png" />
</Frame>