How to integrate Raindrop.io MCP with CrewAI

This guide walks you through connecting Raindrop.io to CrewAI using the Composio tool router. By the end, you'll have a working Raindrop.io agent that can save bookmarks, organize your collections, list your tags, update bookmarks, and delete bookmarks through natural language commands. This guide will help you understand how to give your CrewAI agent real control over a Raindrop.io account through Composio's Raindrop.io MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Raindrop.io is a bookmark manager for saving, organizing, tagging, and searching web content. Use it to keep articles, links, highlights, and collections easy to find later.

11 Tools

Introduction

This guide walks you through connecting Raindrop.io to CrewAI using the Composio tool router. By the end, you'll have a working Raindrop.io agent that can save bookmarks, organize your collections, list your tags, update bookmarks, and delete bookmarks through natural language commands.

This guide will help you understand how to give your CrewAI agent real control over a Raindrop.io account through Composio's Raindrop.io MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Raindrop.io with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndex

TL;DR

Here's what you'll learn:
  • Get a Composio API key and configure your Raindrop.io connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Raindrop.io
  • Build a conversational loop where your agent can execute Raindrop.io operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

What is the Raindrop.io MCP server, and what's possible with it?

The Raindrop.io MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Raindrop.io account. It provides structured and secure access to your bookmarks, collections, tags, and highlights, so your agent can save, find, organize, update, and remove web content on your behalf.

  • Bookmark saving: Have your agent save one or many web links with notes, tags, and collection placement.
  • Search and retrieval: Instruct your agent to search, filter, and sort saved bookmarks, then retrieve a specific bookmark and its highlights.
  • Collection organization: Let the agent create root or nested collections, rename or update them, and remove empty collections you no longer need.
  • Bookmark updates and cleanup: Direct your agent to edit bookmark details, move bookmarks between collections, replace tags, or send unwanted items to Trash.
  • Library overview: Have your agent list your collections and tags with bookmark counts so you can understand how your saved content is organized.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Raindrop.io connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio crewai crewai-tools[mcp] python-dotenv
What's happening:
  • composio connects your agent to Raindrop.io via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools[mcp] includes MCP helpers
  • python-dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model
5

Import dependencies

python
import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Raindrop.io MCP URL
6

Create a Composio Tool Router session for Raindrop.io

python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["raindrop"])

url = session.mcp.url
What's happening:
  • You create a Raindrop.io only session through Composio
  • Composio returns an MCP HTTP URL that exposes Raindrop.io tools
7

Initialize the MCP Server

python
server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )
What's Happening:
  • Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
  • MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
  • Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
  • Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
  • Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.
8

Create a CLI Chatloop and define the Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's Happening:
  • Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
  • Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
  • Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
  • Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
  • Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
  • Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

Here's the complete code to get you started with Raindrop.io and CrewAI:

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["raindrop"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit", "bye"]:
            print("\nGoodbye!")
            break

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Raindrop.io through Composio's Tool Router. The agent can perform Raindrop.io operations through natural language commands.

Next steps:

  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations
TOOLS

Supported Tools

Every Raindrop.io action and event your agent gets out of the box.

Create Bookmark

Save one URL as a Raindrop bookmark with optional organization and notes.

Create Bookmarks

Create 1 to 100 bookmarks in one request with optional organization and notes.

Create Collection

Create a root or nested Raindrop collection.

Delete Bookmark

Move a bookmark to Trash or permanently delete a bookmark already in Trash, requiring the caller to state the intended deletion mode.

Delete Empty Collection

Delete a positive-ID collection after a best-effort check that it contains no bookmarks or child collections.

Get Bookmark

Get one bookmark and its highlights by Raindrop ID.

List Bookmarks

List, search, filter, and sort bookmarks in a collection, all bookmarks, Unsorted, or Trash, returning one page and the next page number when available.

List Collections

List all root and nested collections in the connected Raindrop account, including IDs, parent relationships, titles, and bookmark counts.

List Tags

List tag names and bookmark counts globally or within one collection.

Update Bookmark

Update selected bookmark fields, move it to another collection, or replace its tags while preserving omitted fields.

Update Collection

Change selected properties of an existing Raindrop collection without replacing omitted properties.

FRAMEWORKS

How to build Raindrop.io MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Raindrop.io MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Raindrop.io MCP with Claude Agent SDK

Claude Code logo

Claude Code

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Claude Cowork logo

Claude Cowork

Use Raindrop.io MCP with Claude Cowork

Codex logo

Codex

Use Raindrop.io MCP with Codex

OpenClaw logo

OpenClaw

Use Raindrop.io MCP with OpenClaw

Hermes logo

Hermes

Use Raindrop.io MCP with Hermes

CLI logo

CLI

Use Raindrop.io MCP with CLI

Google ADK logo

Google ADK

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LangChain logo

LangChain

Use Raindrop.io MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Raindrop.io MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Raindrop.io MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Raindrop.io MCP with LlamaIndex

FAQ

Frequently asked questions

With a standalone Raindrop.io MCP server, the agents and LLMs can only access a fixed set of Raindrop.io tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Raindrop.io and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. CrewAI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Raindrop.io tools.

Yes, absolutely. You can configure which Raindrop.io scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Raindrop.io data and credentials are handled as safely as possible.

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