Connect peeps to any MCP-compatible AI tool or agent

peeps supports any MCP-compatible client through two transport options: stdio for local desktop apps and developer tools, and HTTP/SSE for cloud-hosted agents, server-side applications, and any client that connects to remote MCP servers. If your AI tool or framework speaks MCP, you can connect it to peeps.

Stdio transport

Use stdio when you’re running a local application, CLI tool, or development environment that spawns the MCP server as a child process. The client manages the server lifecycle — it starts npx for you and communicates over standard input/output. When to use: Claude Desktop, Cursor, local agent scripts, any MCP client that accepts a command + args config. Set the PEEPS_API_KEY environment variable to authenticate. Here’s a generic stdio config:

{
  "mcpServers": {
    "peeps": {
      "command": "npx",
      "args": ["-y", "@peeps-ai/mcp"],
      "env": {
        "PEEPS_API_KEY": "your-api-key-here"
      }
    }
  }
}

Replace your-api-key-here with your key from app.peeps.ai Settings → API Keys. The exact location of this config block depends on your client — consult its documentation for where to place MCP server definitions.

HTTP/SSE transport

Use HTTP/SSE when you’re running a cloud-hosted agent, a server-side application, or any client that connects to remote (rather than locally spawned) MCP servers. No local Node.js installation is required. When to use: cloud agents, hosted AI platforms, Claude.ai web, LangChain/agent frameworks, any client that accepts a remote server URL.

Setting	Value
Server URL	`https://mcp.peeps.ai/sse`
Authentication	`Authorization: Bearer <your-api-key>` header

A generic HTTP/SSE config looks like this:

{
  "mcpServers": {
    "peeps": {
      "url": "https://mcp.peeps.ai/sse",
      "headers": {
        "Authorization": "Bearer your-api-key-here"
      }
    }
  }
}

The exact field names (url, transport, headers) vary by client — check your tool’s MCP documentation for its specific schema.

LangChain and agent frameworks

If you’re building an agent with LangChain or a similar framework, you can connect to the peeps remote MCP server using the langchain-mcp-adapters package. Install it with pip install langchain-mcp-adapters, then connect like this:

from langchain_mcp_adapters.client import MultiServerMCPClient

client = MultiServerMCPClient({
    "peeps": {
        "url": "https://mcp.peeps.ai/sse",
        "headers": {
            "Authorization": "Bearer your-api-key-here"
        }
    }
})

tools = await client.get_tools()

Once you have tools, pass them to your LangChain agent or chain as you would any other tool list. The peeps tools (search_people, get_person, search_organizations, get_organization, list_contacts) will be available for the agent to call automatically.

The Model Context Protocol specification lists all officially supported client implementations and transport options. If your tool is MCP-compatible and isn’t covered here, the stdio or HTTP/SSE configs above should work with minimal adaptation.

Guides for specific clients

Claude

Step-by-step setup for Claude Desktop and Claude.ai, including config file locations and example prompts.

Cursor

Add peeps to Cursor for AI-powered people search right inside your code editor.

Get Started

MCP Server

Integrations

Help

Connect peeps to any MCP-compatible AI tool or agent

Stdio transport

HTTP/SSE transport

LangChain and agent frameworks

Guides for specific clients

Claude

Cursor

​Stdio transport

​HTTP/SSE transport

​LangChain and agent frameworks

​Guides for specific clients

Claude

Cursor

Stdio transport

HTTP/SSE transport

LangChain and agent frameworks

Guides for specific clients