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Merge pull request #10 from allenzhou101/feature/mcp-client-typescript

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Add MCP Client Typescript Example
This commit is contained in:
David Soria Parra 2025-03-10 10:51:17 +00:00 • committed by GitHub
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13 changed files with 952 additions and 43 deletions
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1
mcp-client-python/.python-version Normal file
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3.10

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mcp-client-python/README.md Normal file
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# An LLM-Powered Chatbot MCP Client written in Python
See the [Building MCP clients](https://modelcontextprotocol.io/tutorials/building-a-client) tutorial for more information.

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mcp-client-python/client.py Normal file
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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()
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])
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 = []
for content in response.content:
if content.type == 'text':
final_text.append(content.text)
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}]")
# Continue conversation with tool results
if hasattr(content, 'text') and content.text:
messages.append({
"role": "assistant",
"content": content.text
})
messages.append({
"role": "user",
"content": result.content
})
# Get next response from Claude
response = self.anthropic.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1000,
messages=messages,
)
final_text.append(response.content[0].text)
return "\n".join(final_text)
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()
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())

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mcp-client-python/pyproject.toml Normal file
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[project]
name = "mcp-client"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"anthropic>=0.40.0",
"mcp>=1.1.1",
"python-dotenv>=1.0.1",
]

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mcp-client-python/uv.lock generated Normal file
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ANTHROPIC_API_KEY=

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mcp-client-typescript/.gitignore vendored Normal file
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/build
/node_modules
.env

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# An LLM-Powered Chatbot MCP Client written in TypeScript
See the [Building MCP clients](https://modelcontextprotocol.io/tutorials/building-a-client) tutorial for more information.

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mcp-client-typescript/index.ts Normal file
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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(); // load environment variables from .env
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() {
// Initialize Anthropic client and MCP client
this.anthropic = new Anthropic({
apiKey: ANTHROPIC_API_KEY,
});
this.mcp = new Client({ name: "mcp-client-cli", version: "1.0.0" });
}
async connectToServer(serverScriptPath: string) {
/**
* Connect to an MCP server
*
* @param serverScriptPath - Path to the server script (.py or .js)
*/
try {
// Determine script type and appropriate command
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;
// Initialize transport and connect to server
this.transport = new StdioClientTransport({
command,
args: [serverScriptPath],
});
this.mcp.connect(this.transport);
// List available tools
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;
}
}
async processQuery(query: string) {
/**
* Process a query using Claude and available tools
*
* @param query - The user's input query
* @returns Processed response as a string
*/
const messages: MessageParam[] = [
{
role: "user",
content: query,
},
];
// Initial Claude API call
const response = await this.anthropic.messages.create({
model: "claude-3-5-sonnet-20241022",
max_tokens: 1000,
messages,
tools: this.tools,
});
// Process response and handle tool calls
const finalText = [];
const toolResults = [];
for (const content of response.content) {
if (content.type === "text") {
finalText.push(content.text);
} else if (content.type === "tool_use") {
// Execute tool call
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)}]`,
);
// Continue conversation with tool results
messages.push({
role: "user",
content: result.content as string,
});
// Get next response from Claude
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");
}
async chatLoop() {
/**
* Run an interactive chat loop
*/
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() {
/**
* Clean up resources
*/
await this.mcp.close();
}
}
async function main() {
if (process.argv.length < 3) {
console.log("Usage: node build/index.js <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();

654
mcp-client-typescript/package-lock.json generated Normal file
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25
mcp-client-typescript/package.json Normal file
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15
mcp-client-typescript/tsconfig.json Normal file
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99
weather-server-typescript/package-lock.json generated
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