How to integrate Affinda MCP with CrewAI

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Introduction

This guide walks you through connecting Affinda to CrewAI using the Composio tool router. By the end, you'll have a working Affinda agent that can extract invoice data from uploaded pdf, delete a document no longer needed, create a new tag for hr documents through natural language commands.

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

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

Also integrate Affinda with

TL;DR

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

The Affinda MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Affinda account. It provides structured and secure access to your document processing workflows, so your agent can upload files, extract data, organize workspaces, label documents, and automate annotation management on your behalf.

  • AI-powered document upload and extraction: Instantly have your agent upload new documents for parsing and extract structured data from various formats using Affinda's advanced AI models.
  • Workspace and collection management: Let your agent create, group, and organize documents into collections and workspaces, keeping your document processing streamlined and organized.
  • Automated annotation updates: Empower your agent to batch update or modify multiple document annotations in a single request, saving you time on manual corrections.
  • Document tagging and organization: Direct your agent to create tags and label documents, making it easy to categorize and quickly retrieve important files.
  • Effortless cleanup and resource management: Have your agent delete unwanted documents or collections, ensuring your Affinda account stays tidy and relevant at all times.

Supported Tools & Triggers

Tools
Add Tag to DocumentsTool to add a tag to multiple documents in a single operation.
Batch Update AnnotationsBatch update multiple document annotations in a single API call.
Create API UserTool to create a new API user within an organization.
Batch Create AnnotationsBatch create multiple document annotations in a single API call.
Create CollectionTool to create a new collection.
Create Data Field For CollectionTool to create a data field for a collection along with a new data point.
Create Data SourceTool to create a custom mapping data source.
Create Data Source ValueTool to add a new value to a mapping data source.
Create DocumentUpload a document to Affinda for parsing and data extraction.
Create Document TypeTool to create a new document type in the specified organization.
Create ExtractorTool to create a new extractor.
Create Document from DataCreate a document from structured resume or job description data for Search & Match.
Create IndexTool to create a new index for search and match functionality.
Create InvitationTool to create a new organization invitation.
Create Job Description SearchSearch through parsed job descriptions using custom criteria or resume matching.
Create Job Description Search Embed URLTool to create and return a signed URL for the embeddable job description search tool.
Create OrganizationTool to create a new organization.
Create RESTHook SubscriptionTool to create a new RESTHook subscription.
Create Resume SearchTool to search through parsed resumes using three methods: match to a job description, match to a resume, or custom criteria.
Create Resume Search Embed URLTool to create and return a signed URL for the embeddable resume search tool.
Create TagCreates a new tag in the specified workspace.
Create Validation ResultCreate a validation result for document annotations in Affinda.
Batch Create Validation ResultsBatch create multiple validation results for document annotations in a single API call.
Create WorkspaceTool to create a new workspace.
Create Workspace MembershipTool to add a user to a workspace by creating a membership.
Batch Delete AnnotationsBatch delete multiple document annotations in a single API call.
Delete CollectionPermanently delete a collection from Affinda by its identifier.
Delete Data SourcePermanently delete a mapping data source from the database by its identifier.
Delete Data Source ValueTool to delete a specific value from a mapping data source.
Delete DocumentTool to delete a specific document by its ID.
Delete Document TypeTool to permanently delete a document type by its identifier.
Delete IndexTool to permanently delete an index from Affinda by its name.
Delete InvitationTool to delete an invitation by its identifier.
Delete OrganizationPermanently deletes an organization from Affinda.
Delete Resthook SubscriptionTool to delete a specific resthook subscription by ID.
Delete TagPermanently delete a tag from Affinda by its ID.
Delete Validation ResultsDelete multiple validation results in a single API call.
Delete WorkspaceTool to delete a specific workspace by its ID.
Delete Workspace MembershipTool to remove a user from a workspace by membership ID.
Get All API UsersTool to retrieve a list of all API users.
Get All Document SplittersTool to get a list of all document splitters.
Get All InvitationsTool to retrieve all invitations you created or sent to you.
Get Organization MembershipsRetrieve all organization memberships across the account.
Get TagsTool to list all tags.
Get All Validation ResultsTool to list validation results for documents.
Get Workspace MembershipsRetrieve all workspace memberships across the account.
Get AnnotationsRetrieves all annotations for a specific document.
Get CollectionTool to retrieve details of a specific collection by its ID.
Get CollectionsTool to retrieve a list of all collections.
Get Data SourceTool to retrieve details of a specific mapping data source by its identifier.
Get Data Source ValueTool to retrieve a specific value from a mapping data source.
Get Data Source ValuesTool to retrieve all values from a mapping data source.
Get DocumentRetrieve full details and parsed data for a specific document by its identifier.
Get Document RedactedTool to retrieve the redacted version of a document as a PDF file.
Get DocumentsTool to retrieve a list of all documents.
Get Document SplitterTool to retrieve details of a specific document splitter by its identifier.
Get Document TypeTool to retrieve details of a specific document type by its ID.
Get Document Type JSON SchemaTool to generate a JSON schema from a document type by its identifier.
Get Document Type Pydantic ModelsTool to generate Pydantic model code from a document type's schema.
Get Document TypesRetrieve all document types accessible to the authenticated user.
Get ExtractorTool to retrieve detailed information about a specific extractor by its identifier.
Get ExtractorsRetrieve all extractors available for an organization.
Get Index DocumentsTool to retrieve all indexed documents for a specific index.
Get InvitationTool to retrieve details of a specific organization invitation by its identifier.
Get Job Description Search ConfigTool to get the configuration for the logged in user's embeddable job description search tool.
Get MappingTool to retrieve a specific mapping by its identifier.
Get OrganizationTool to retrieve details of a specific organization by its ID.
Get Organization MembershipTool to retrieve details of a specific organization membership by its ID.
Get OrganizationsRetrieves all organizations accessible to the authenticated user.
Get Resthook SubscriptionTool to retrieve details of a specific resthook subscription by its ID.
Get RESTHook SubscriptionsTool to retrieve a list of all RESTHook subscriptions.
Get TagTool to retrieve details of a specific tag by its ID.
Get Usage by WorkspaceRetrieves monthly document processing usage statistics for a specific workspace.
Get WorkspaceTool to retrieve details of a specific workspace by its ID.
Get Workspace MembershipTool to retrieve details of a specific workspace membership by its ID.
Get WorkspacesTool to retrieve a list of all workspaces.
List Data PointsTool to retrieve all data points.
List Data SourcesTool to retrieve the list of all custom mapping data sources.
List IndexesTool to retrieve a list of all search indexes.
List MappingsTool to retrieve the list of all custom data mappings.
List Occupation GroupsTool to retrieve the list of searchable occupation groups.
List Resume Search ConfigTool to get the configuration for the logged in user's embeddable resume search tool.
List Resume Search Job Title SuggestionsTool to get job title suggestions based on provided job title(s).
List Resume Search Skill SuggestionsTool to get skill suggestions based on provided skills.
Remove Tag from DocumentsRemove a tag from multiple documents in a single batch operation.
Replace Data Source ValuesTool to completely replace all values in a mapping data source.
Split Document PagesSplit a document into multiple documents by dividing its pages.
Update AnnotationTool to update data of a single annotation in Affinda.
Update CollectionTool to update specific fields of a collection.
Update Data Field For CollectionTool to update a data field configuration for a collection's data point.
Update Data Source ValueTool to update an existing value in a mapping data source.
Update DocumentTool to update specific fields of a document.
Update Document DataUpdate parsed data for a resume or job description document in Affinda.
Update Document TypeTool to update a document type by its identifier.
Update ExtractorTool to update specific fields of an extractor.
Update IndexTool to update the name of an existing search index.
Update InvitationTool to update an organization invitation's role.
Update Job Description Search ConfigTool to update the configuration for the logged in user's embeddable job description search tool.
Update MappingTool to update a specific mapping's settings.
Update OrganizationTool to update specific fields of an organization.
Update Organization MembershipTool to update an organization membership's role.
Update RESTHook SubscriptionTool to update an existing RESTHook subscription.
Update Resume Search ConfigTool to update the configuration for the logged in user's embeddable resume search tool.
Update TagTool to update data of a tag.
Update WorkspaceTool to update specific fields of a workspace.

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

Prerequisites

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

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.

Install dependencies

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

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

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 Affinda MCP URL

Create a Composio Tool Router session for Affinda

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

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

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.

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 Affinda 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=["affinda"],
)
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 Affinda through Composio's Tool Router. The agent can perform Affinda 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

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FAQ

What are the differences in Tool Router MCP and Affinda MCP?

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

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

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

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