A TypeScript implementation of a Model Context Protocol (MCP) client for LLM agents.
npm install mcp-client
The most common way to use MCP Client is with standard MCP servers via npx. Create a configuration file (mcp-config.json
):
{
"mcpServers": {
"memory": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-memory"
]
},
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/path/to/allowed/directory"
]
},
"brave-search": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-brave-search"
],
"env": {
"BRAVE_API_KEY": "your-api-key"
}
}
}
}
Then use the MCPConnectionManager to connect to your servers:
import { MCPConnectionManager } from 'mcp-client';
const manager = new MCPConnectionManager();
await manager.initialize('./mcp-config.json');
// Get clients for specific servers
const memoryClient = manager.getClient('memory');
const fsClient = manager.getClient('filesystem');
// Use tools from the servers
const memoryTools = await memoryClient?.listTools();
const fsTools = await fsClient?.listTools();
// Clean up when done
await manager.cleanup();
The client is designed to work seamlessly with Claude's native tool calling:
import { Anthropic } from '@anthropic-ai/sdk';
import { MCPConnectionManager } from 'mcp-client';
// Initialize Claude and MCP
const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
const manager = new MCPConnectionManager();
await manager.initialize('./mcp-config.json');
// Get a client
const memoryClient = manager.getClient('memory');
const tools = await memoryClient?.listTools();
// Use with Claude
const response = await anthropic.messages.create({
model: 'claude-3-sonnet-20240229',
max_tokens: 1024,
messages: [{ role: 'user', content: 'your prompt' }],
tools: tools?.map(tool => ({
name: tool.name,
description: tool.description || '',
parameters: tool.inputSchema
}))
});
// Handle tool calls
if (!response.content[0].text && response.content[0].type === 'tool_call') {
const toolCall = response.content[0].tool_calls[0];
const result = await memoryClient?.callTool(
toolCall.name,
toolCall.parameters
);
// Send result back to Claude...
}
If you need more control, you can use the client directly:
import { MCPClient, StdioTransport } from 'mcp-client';
// Create a transport
const transport = new StdioTransport();
// Create and connect client
const client = new MCPClient({ transport });
await client.connect();
// List available tools
const tools = await client.listTools();
// Call a tool
const result = await client.callTool('tool-name', {
// tool parameters
});
Common MPC servers available via npx:
@modelcontextprotocol/server-memory
- Knowledge graph operations@modelcontextprotocol/server-filesystem
- File system operations@modelcontextprotocol/server-brave-search
- Web search capabilities@modelcontextprotocol/server-puppeteer
- Web automation@modelcontextprotocol/server-fetch
- HTTP requestsCheck the /examples
directory for complete examples:
memory-app
- Using the Memory Server for knowledge graph operationsllm-app
- Basic LLM app with MCP toolsllm-app-tools
- Advanced LLM app with Claude native tool callingclass MCPConnectionManager {
initialize(configPath: string): Promise<void>;
getClient(serverName: string): MCPClient | undefined;
cleanup(): Promise<void>;
}
class MCPClient {
constructor(options: MCPClientOptions);
// Connection
connect(): Promise<void>;
close(): Promise<void>;
// Tools
listTools(): Promise<Tool[]>;
callTool(name: string, params: Record<string, any>): Promise<any>;
// Resources
listResources(): Promise<Resource[]>;
readResource(uri: string): Promise<string | Buffer>;
// Prompts
listPrompts(): Promise<Prompt[]>;
getPrompt(name: string, args?: Record<string, any>): Promise<string>;
}
Contributions are welcome! Please feel free to submit a Pull Request.
MIT
{
"mcpServers": {
"memory": {
"env": {},
"args": [
"-y",
"@modelcontextprotocol/server-memory"
],
"command": "npx"
},
"filesystem": {
"env": {},
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/path/to/allowed/directory"
],
"command": "npx"
},
"brave-search": {
"env": {
"BRAVE_API_KEY": "your-api-key"
},
"args": [
"-y",
"@modelcontextprotocol/server-brave-search"
],
"command": "npx"
}
}
}
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