# How to integrate Abuselpdb MCP with CrewAI

```json
{
  "title": "How to integrate Abuselpdb MCP with CrewAI",
  "toolkit": "Abuselpdb",
  "toolkit_slug": "abuselpdb",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/abuselpdb/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/abuselpdb/framework/crew-ai.md",
  "updated_at": "2026-10-12T14:34:36.609Z"
}
```

## Introduction

This guide walks you through connecting Abuselpdb to CrewAI using the Composio tool router. By the end, you'll have a working Abuselpdb agent that can check if this ip is blacklisted, report multiple suspicious ips from csv, get abuse reports for a specific ip through natural language commands.
This guide will help you understand how to give your CrewAI agent real control over a Abuselpdb account through Composio's Abuselpdb MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Abuselpdb with

- [Claude Code](https://composio.dev/toolkits/abuselpdb/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/abuselpdb/framework/claude-cowork)
- [OpenAI Agents SDK](https://composio.dev/toolkits/abuselpdb/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/abuselpdb/framework/claude-agents-sdk)
- [Codex](https://composio.dev/toolkits/abuselpdb/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/abuselpdb/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/abuselpdb/framework/hermes-agent)
- [Atomic Agent](https://composio.dev/toolkits/abuselpdb/framework/atomic-agent)
- [Google ADK](https://composio.dev/toolkits/abuselpdb/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/abuselpdb/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/abuselpdb/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/abuselpdb/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/abuselpdb/framework/llama-index)
- [Pydantic AI](https://composio.dev/toolkits/abuselpdb/framework/pydantic-ai)
- [AutoGen](https://composio.dev/toolkits/abuselpdb/framework/autogen)

## TL;DR

Here's what you'll learn:
- Get a Composio API key and configure your Abuselpdb connection
- Set up CrewAI with an MCP enabled agent
- Create a Tool Router session or standalone MCP server for Abuselpdb
- Build a conversational loop where your agent can execute Abuselpdb 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 Abuselpdb MCP server, and what's possible with it?

The AbuseIPDB MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your AbuseIPDB account. It provides structured and secure access to IP reputation data and abuse reporting tools, so your agent can check IP and network reputations, retrieve abuse reports, review blacklists, submit bulk reports, and clear address reports on your behalf.
- IP reputation checks: Have your agent check an IP address for reported malicious activity and reputation details.
- Abuse report retrieval: Instruct your agent to retrieve available abuse reports for a specific IP address.
- Malicious IP blacklist access: Let the agent retrieve a list of the most reported malicious IP addresses in AbuseIPDB.
- Network range investigation: Direct your agent to check the reputation of IP addresses across a CIDR range.
- Report submission and cleanup: Have your agent submit multiple abuse reports from a CSV file or clear all reports associated with a specific IP address.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ABUSELPDB_BLACKLIST` | Retrieve IP Blacklist | Retrieves a list of the most reported malicious IP addresses from AbuseIPDB's database. Use this tool to build dynamic blocklists, threat intelligence feeds, or firewall rules. The blacklist is updated hourly and contains IPs with high abuse confidence scores. Free accounts receive up to 10,000 IPs. Paid subscriptions unlock filtering options (confidenceMinimum, country filters) and higher limits (up to 500,000 IPs). |
| `ABUSELPDB_BULK_REPORT` | Bulk Report | Submit multiple IP abuse reports to AbuseIPDB in bulk via CSV upload. Use this when you need to report many malicious IPs at once instead of one-by-one. Returns the count of successfully saved reports and details about any invalid entries. |
| `ABUSELPDB_CHECK_BLOCK` | Check Block | Tool to check the reputation of all IP addresses in a CIDR range. Use when you need aggregated abuse data for a network block. |
| `ABUSELPDB_CHECK_IP` | Check IP Reputation | Tool to check the reputation of an IP address. Use when you need to determine if an IP address has been reported for abusive activity within a specified look-back period. Example: CheckIp(ipAddress='8.8.8.8', maxAgeInDays=90). |
| `ABUSELPDB_CLEAR_ADDRESS` | Clear Address Reports | Tool to remove all reports associated with a specific IP address. Use when you need to purge your own abuse records after verifying control of the IP. |
| `ABUSELPDB_GET_REPORTS` | Get Abuse Reports | Retrieve abuse reports for a specific IP address from AbuseIPDB. Use this tool to view the history of abuse complaints filed against an IP address, including the reported abuse categories, reporter details, and timestamps. Supports pagination for IPs with many reports. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Abuselpdb MCP server is an implementation of the Model Context Protocol that connects your AI agent to Abuselpdb. It provides structured and secure access so your agent can perform Abuselpdb operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

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

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

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

### 3. Set up environment variables

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
```bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here
```

### 4. Import dependencies

**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 Abuselpdb MCP URL
```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")
```

### 5. Create a Composio Tool Router session for Abuselpdb

**What's happening:**
- You create a Abuselpdb only session through Composio
- Composio returns an MCP HTTP URL that exposes Abuselpdb tools
```python
composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["abuselpdb"])

url = session.mcp.url
```

### 6. Initialize the MCP Server

**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.
```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,
    )
```

### 7. Create a CLI Chatloop and define the Crew

**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.
```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")
```

## Complete Code

```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=["abuselpdb"],
)
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 Abuselpdb through Composio's Tool Router. The agent can perform Abuselpdb 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

## How to build Abuselpdb MCP Agent with another framework

- [Claude Code](https://composio.dev/toolkits/abuselpdb/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/abuselpdb/framework/claude-cowork)
- [OpenAI Agents SDK](https://composio.dev/toolkits/abuselpdb/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/abuselpdb/framework/claude-agents-sdk)
- [Codex](https://composio.dev/toolkits/abuselpdb/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/abuselpdb/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/abuselpdb/framework/hermes-agent)
- [Atomic Agent](https://composio.dev/toolkits/abuselpdb/framework/atomic-agent)
- [Google ADK](https://composio.dev/toolkits/abuselpdb/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/abuselpdb/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/abuselpdb/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/abuselpdb/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/abuselpdb/framework/llama-index)
- [Pydantic AI](https://composio.dev/toolkits/abuselpdb/framework/pydantic-ai)
- [AutoGen](https://composio.dev/toolkits/abuselpdb/framework/autogen)

## Related Toolkits

- [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure.
- [Codeinterpreter](https://composio.dev/toolkits/codeinterpreter) - Codeinterpreter is a Python-based coding environment with built-in data analysis and visualization. It lets you instantly run scripts, plot results, and prototype solutions inside supported platforms.
- [GitHub](https://composio.dev/toolkits/github) - GitHub is a code hosting platform for version control and collaborative software development. It streamlines project management, code review, and team workflows in one place.
- [1password](https://composio.dev/toolkits/_1password) - 1Password is a password manager and digital vault for storing logins, secrets, notes, and secure documents. It helps individuals and teams protect credentials, share access safely, and reduce password risk.
- [Ably](https://composio.dev/toolkits/ably) - Ably is a real-time messaging platform for live chat and data sync in modern apps. It offers global scale and rock-solid reliability for seamless, instant experiences.
- [Alchemy](https://composio.dev/toolkits/alchemy) - Alchemy is a blockchain development platform offering APIs and tools for Ethereum apps. It simplifies building and scaling Web3 projects with robust infrastructure.
- [Algolia](https://composio.dev/toolkits/algolia) - Algolia is a hosted search API that powers lightning-fast, relevant search experiences for web and mobile apps. It helps developers deliver instant, typo-tolerant, and scalable search without complex infrastructure.
- [Anakin](https://composio.dev/toolkits/anakin_io) - Anakin is a web automation platform offering scraping, crawling, search, and browser automation. Use it to extract structured data, automate site tasks, and monitor web changes reliably.
- [Anchor browser](https://composio.dev/toolkits/anchor_browser) - Anchor browser is a developer platform for AI-powered web automation. It transforms complex browser actions into easy API endpoints for streamlined web interaction.
- [Apiflash](https://composio.dev/toolkits/apiflash) - Apiflash is a website screenshot API for programmatically capturing web pages. It delivers high-quality screenshots on demand for automation, monitoring, or reporting.
- [Apiverve](https://composio.dev/toolkits/apiverve) - Apiverve delivers a suite of powerful APIs that simplify integration for developers. It's designed for reliability and scalability so you can build faster, smarter applications without the integration headache.
- [Appcircle](https://composio.dev/toolkits/appcircle) - Appcircle is an enterprise-grade mobile CI/CD platform for building, testing, and publishing mobile apps. It streamlines mobile DevOps so teams ship faster and with more confidence.
- [Appdrag](https://composio.dev/toolkits/appdrag) - Appdrag is a cloud platform for building websites, APIs, and databases with drag-and-drop tools and code editing. It accelerates development and iteration by combining hosting, database management, and low-code features in one place.
- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Authyo](https://composio.dev/toolkits/authyo) - Authyo is a REST API service for passwordless authentication, OTP verification, session management, and transactional notifications. Use it to add secure login flows and user verification without building auth infrastructure from scratch.
- [AWS Marketplace MCP](https://composio.dev/toolkits/aws_marketplace_mcp) - AWS Marketplace MCP provides MCP access to AWS Marketplace's cloud software, data, and services catalog. Use it to discover, compare, and evaluate 30K+ AWS Marketplace listings faster.
- [Azure Monitor Activity Log](https://composio.dev/toolkits/azure_monitor_activity_log) - Azure Monitor Activity Log is Azure's service that records management and control-plane events for a subscription. Use it to audit changes, troubleshoot issues, and track resource operations across your Azure resources.
- [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure.
- [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code.
- [Bench](https://composio.dev/toolkits/bench) - Bench is a benchmarking tool for automated performance measurement and analysis. It helps you quickly evaluate, compare, and track your systems or workflows.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Abuselpdb MCP?

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

### Can I use Tool Router MCP with CrewAI?

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 Abuselpdb tools.

### Can I manage the permissions and scopes for Abuselpdb while using Tool Router?

Yes, absolutely. You can configure which Abuselpdb 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.

### How safe is my data with Composio Tool Router?

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 Abuselpdb data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)