# How to integrate Algodocs MCP with CrewAI

```json
{
  "title": "How to integrate Algodocs MCP with CrewAI",
  "toolkit": "Algodocs",
  "toolkit_slug": "algodocs",
  "framework": "CrewAI",
  "framework_slug": "crew-ai",
  "url": "https://composio.dev/toolkits/algodocs/framework/crew-ai",
  "markdown_url": "https://composio.dev/toolkits/algodocs/framework/crew-ai.md",
  "updated_at": "2026-10-12T12:48:56.583Z"
}
```

## Introduction

This guide walks you through connecting Algodocs to CrewAI using the Composio tool router. By the end, you'll have a working Algodocs agent that can extract invoice totals from uploaded pdfs, pull key data fields from bank statements, summarize contract terms from scanned documents through natural language commands.
This guide will help you understand how to give your CrewAI agent real control over a Algodocs account through Composio's Algodocs MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Algodocs with

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

## TL;DR

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

The Algodocs MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Algodocs account. It provides structured and secure access to your user information, document data extractors, and folders, so your agent can view your account details, list available extractors, and browse your folders on your behalf.
- Account information: Have your agent retrieve your current Algodocs user information whenever you need account context.
- Extractor discovery: Let the agent list the document data extractors available in your Algodocs account.
- Folder browsing: Direct your agent to list your Algodocs folders so you can see how your documents are organized.
- Workspace overview: Instruct your agent to summarize your available extractors and folders alongside your current user information.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ALGODOCS_GET_ME` | Get Current User | Tool to retrieve current user information. Use after authenticating with your API key to verify identity. |
| `ALGODOCS_LIST_EXTRACTORS` | List document data extractors | Tool to list document data extractors. Use when you need identifiers and names of all extractors in your AlgoDocs account. |
| `ALGODOCS_LIST_FOLDERS` | List folders | Tool to list folders. Use when you need to retrieve all folder IDs and their hierarchy. Must be called after obtaining an API key. |

## Supported Triggers

None listed.

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

The Algodocs MCP server is an implementation of the Model Context Protocol that connects your AI agent to Algodocs. It provides structured and secure access so your agent can perform Algodocs 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 Algodocs 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 Algodocs 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 Algodocs 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 Algodocs

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

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=["algodocs"],
)
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 Algodocs through Composio's Tool Router. The agent can perform Algodocs 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 Algodocs MCP Agent with another framework

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

## Related Toolkits

- [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.
- [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze.
- [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization.
- [ActiveMerge](https://composio.dev/toolkits/active_merge) - ActiveMerge is a document automation platform for generating documents, images, merged files, and e-signature workflows. It helps teams turn templates and stored files into finished, shareable documents faster.
- [Affinda](https://composio.dev/toolkits/affinda) - Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data.
- [Agility cms](https://composio.dev/toolkits/agility_cms) - Agility CMS is a headless content management system for building and managing digital experiences across platforms. It lets teams update content quickly and deliver omnichannel experiences with ease.
- [Airparser](https://composio.dev/toolkits/airparser) - Airparser is an AI-powered parser for extracting structured data from emails, documents, PDFs, and files. It turns messy inbound content into clean fields your apps and workflows can use.
- [Api2pdf](https://composio.dev/toolkits/api2pdf) - Api2Pdf is a REST API for generating PDFs from HTML, URLs, and documents using powerful engines like wkhtmltopdf and Headless Chrome. It streamlines document conversion and automation for developers and businesses.
- [Archive.org](https://composio.dev/toolkits/archive_org) - Archive.org is the Internet Archive's digital library of websites, books, audio, video, and images. Use it to preserve, explore, and access historical web captures and public-domain media.
- [Aryn](https://composio.dev/toolkits/aryn) - Aryn is an AI-powered platform for parsing, extracting, and analyzing data from unstructured documents. Use it to automate document processing and unlock actionable insights from your files.
- [Boldsign](https://composio.dev/toolkits/boldsign) - Boldsign is a digital eSignature platform for sending, signing, and tracking documents online. Organizations use it to automate agreements and manage legally binding workflows efficiently.
- [Boloforms](https://composio.dev/toolkits/boloforms) - BoloForms is an eSignature platform built for small businesses, offering unlimited signatures, templates, and forms. It simplifies digital document signing and team collaboration at a predictable, fixed price.
- [Box](https://composio.dev/toolkits/box) - Box is a cloud content management and file sharing platform for businesses. It helps teams securely store, organize, and collaborate on files from anywhere.
- [Box MCP](https://composio.dev/toolkits/box_mcp) - Box MCP is Box's hosted MCP server that exposes enterprise files, folders, and metadata. It provides secure, centralized access to corporate content for search and management.
- [Carbone](https://composio.dev/toolkits/carbone) - Carbone is a blazing-fast report generator that turns JSON data into PDFs, Word docs, spreadsheets, and more using flexible templates. It lets you automate document creation at scale with minimal code.
- [Castingwords](https://composio.dev/toolkits/castingwords) - CastingWords is a transcription service specializing in human-powered, accurate transcripts via a simple API. Get seamless audio-to-text conversion for interviews, meetings, podcasts, and more.
- [CertSeal](https://composio.dev/toolkits/cert_seal) - CertSeal is an API platform for issuing and managing verifiable digital certificates. Use it to automate trusted certificate workflows for recipients, batches, designs, and webhooks.
- [Cloudconvert](https://composio.dev/toolkits/cloudconvert) - CloudConvert is a powerful file conversion service supporting over 200 file formats. It streamlines converting, compressing, and managing documents, media, and more, all in one place.
- [Cloudlayer](https://composio.dev/toolkits/cloudlayer) - Cloudlayer is a document and asset generation service for creating PDFs and images via API or SDKs. It lets you automate high-quality doc creation, saving dev time and reducing manual work.
- [CloudPDF](https://composio.dev/toolkits/cloud_pdf) - CloudPDF is an API platform for creating, uploading, managing, and sharing PDF documents. Use it to build secure PDF workflows with webhooks, share links, and access control.

## Frequently Asked Questions

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

With a standalone Algodocs MCP server, the agents and LLMs can only access a fixed set of Algodocs tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Algodocs 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 Algodocs tools.

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

Yes, absolutely. You can configure which Algodocs 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 Algodocs data and credentials are handled as safely as possible.

---
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