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---
title: "For Client Developers"
description: "Get started building your own client that can integrate with all MCP servers."
---
In this tutorial, you'll learn how to build a LLM-powered chatbot client that connects to MCP servers. It helps to have gone through the [Server quickstart](/quickstart/server) that guides you through the basic of building your first server.
<Tabs>
<Tab title="Python">
[You can find the complete code for this tutorial here.](https://github.com/modelcontextprotocol/quickstart-resources/tree/main/mcp-client-python)
## 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
rm hello.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
tool_results = []
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)
tool_results.append({"call": tool_name, "result": result})
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 .../weather/src/weather/server.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="/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
</Tab>
<Tab title="Node">
[You can find the complete code for this tutorial here.](https://github.com/modelcontextprotocol/quickstart-resources/tree/main/mcp-client-typescript)
## System Requirements
Before starting, ensure your system meets these requirements:
- Mac or Windows computer
- Node.js 16 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
</Tab>
<Tab title="Java">
<Note>
This is a quickstart demo based on Spring AI MCP auto-configuration and boot starters.
To learn how to create sync and async MCP Clients manually, consult the [Java SDK Client](/sdk/java/mcp-client) documentation
</Note>
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-mcp-client-spring-boot-starter</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-anthropic-spring-boot-starter</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
anthropic:
api-key: ${ANTHROPIC_API_KEY}
```
This activates the `spring-ai-mcp-client-spring-boot-starter` to create one or more `McpClient`s based on the provided server configuration.
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.")
.defaultTools((Object[]) mcpToolAdapter.toolCallbacks())
.defaultAdvisors(new MessageChatMemoryAdvisor(new InMemoryChatMemory()))
.build();
```
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.
</Tab>
</Tabs>
## Next steps
<CardGroup cols={2}>
<Card
title="Example servers"
icon="grid"
href="/examples"
>
Check out our gallery of official MCP servers and implementations
</Card>
<Card
title="Clients"
icon="cubes"
href="/clients"
>
View the list of clients that support MCP integrations
</Card>
<Card
title="Building MCP with LLMs"
icon="comments"
href="/tutorials/building-mcp-with-llms"
>
Learn how to use LLMs like Claude to speed up your MCP development
</Card>
<Card
title="Core architecture"
icon="sitemap"
href="/docs/concepts/architecture"
>
Understand how MCP connects clients, servers, and LLMs
</Card>
</CardGroup>
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