How to integrate Finage MCP with CrewAI

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Introduction

This guide walks you through connecting Finage to CrewAI using the Composio tool router. By the end, you'll have a working Finage agent that can show real-time quote for aapl stock, summarize today's news for tsla, get last week's price history for msft, check if us stock market is open now through natural language commands.

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

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

TL;DR

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

The Finage MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Finage account. It provides structured and secure access to real-time and historical financial market data, so your agent can perform actions like fetching live stock quotes, analyzing market trends, retrieving news, and monitoring market status on your behalf.

  • Real-time stock quotes and market snapshots: Instantly get up-to-date price quotes, volume, bid/ask details, or aggregate snapshots for one or multiple stocks at once.
  • Historical and end-of-day data retrieval: Have your agent pull detailed historical or end-of-day OHLCV data for any stock symbol to analyze trends or generate reports.
  • Market news and updates analysis: Stay on top of the latest financial news, filtered by specific stocks, to inform investment decisions or research market events.
  • Market status and exchange monitoring: Check if major stock markets are open or closed, review trading hours, and plan automated trades based on real-time market status.
  • Tick-by-tick and previous close data access: Dive into granular tick-level trade data or review the previous day's close for any symbol to support high-frequency trading or day-over-day analysis.

Supported Tools & Triggers

Tools
Get Stock QuoteThis tool retrieves real-time stock quote information for a given stock symbol.
Get Stock End-of-Day DataThis tool retrieves the end-of-day data for a specific stock symbol.
Get Stock Historical DataThis tool will fetch historical daily stock data for a given stock symbol between specified dates.
Get Stock Last QuoteThis tool fetches the latest quote information for a specific stock symbol, providing real-time bid and ask prices, along with their respective sizes and a timestamp.
Get Stock Market AggregatesThis tool retrieves aggregated ohlcv (open, high, low, close, volume) data for stocks over specified time periods.
Get Stock Market NewsThis tool retrieves the latest market news from finage's api.
Get Stock Market SnapshotThis tool provides a comprehensive snapshot of the us stock market data, allowing users to get the latest quotes and trades for multiple stocks in a single api request.
Get Stock Market StatusThis tool retrieves the current market status for various stock exchanges by determining if markets are open or closed.
Get Stock Previous Close DataThis tool retrieves the previous day's closing data for a specific stock symbol.
Get Stock Tick DataThe stock tick data api provides detailed tick-by-tick trading data for a specific stock symbol.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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 Finage 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 python-dotenv
What's happening:
  • composio connects your agent to Finage via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools 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
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
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 Finage MCP URL

Create a Composio Tool Router session for Finage

python
composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
session = composio.create(
    user_id=os.getenv("USER_ID"),
    toolkits=["finage"],
)
url = session.mcp.url
What's happening:
  • You create a Finage only session through Composio
  • Composio returns an MCP HTTP URL that exposes Finage tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Finage Assistant",
    goal="Help users interact with Finage through natural language commands",
    backstory=(
        "You are an expert assistant with access to Finage tools. "
        "You can perform various Finage operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Finage MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Finage operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Finage related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_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:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_finage_agent.py

Complete Code

Here's the complete code to get you started with Finage and CrewAI:

python
# file: crewai_finage_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

def main():
    # Initialize Composio and create a Finage session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["finage"],
    )
    url = session.mcp.url

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

    # Create Finage assistant agent
    toolkit_agent = Agent(
        role="Finage Assistant",
        goal="Help users interact with Finage through natural language commands",
        backstory=(
            "You are an expert assistant with access to Finage tools. "
            "You can perform various Finage operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Finage operations.\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"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Finage related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

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

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Finage through Composio's Tool Router. The agent can perform Finage 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 Finage MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Finage MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Finage MCP with Claude Agent SDK

Google ADK

Google ADK

Use Finage MCP with Google ADK

LangChain

LangChain

Use Finage MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Finage MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Finage MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Finage MCP with LlamaIndex

FAQ

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

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

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

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

Used by agents from

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ASU
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Context
ASU
Letta
glean
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Agent.ai
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Entelligence
Rolai

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