How to integrate Codacy MCP with LangChain

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Introduction

This guide walks you through connecting Codacy to LangChain using the Composio tool router. By the end, you'll have a working Codacy agent that can list all projects in your codacy account, show details for your active codacy organizations, enumerate repositories for a specific organization through natural language commands.

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

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

Also integrate Codacy with

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Codacy project to Composio
  • Create a Tool Router MCP session for Codacy
  • Initialize an MCP client and retrieve Codacy tools
  • Build a LangChain agent that can interact with Codacy
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

What is the Codacy MCP server, and what's possible with it?

The Codacy MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Codacy account. It provides structured and secure access to your code quality dashboard, so your agent can perform actions like listing projects, managing API tokens, checking organization repositories, and retrieving user account details on your behalf.

  • Automated project listing and discovery: Instantly fetch a comprehensive list of all projects accessible to your Codacy account for quick overview and navigation.
  • Organization and repository management: Ask your agent to enumerate all organizations you belong to and drill into the repositories associated with each, helping you keep track of where your code lives.
  • API token lifecycle management: Effortlessly create or delete API tokens as needed, making it easy to manage secure integrations and access controls without leaving your flow.
  • User account insights and verification: Retrieve account details to confirm authentication, audit user info, or set up new integrations with confidence.

Supported Tools & Triggers

Tools
Create API TokenCreates a new account API token for the authenticated user.
Delete API TokenTool to delete a specific API token from the authenticated user's account.
Get Account DetailsTool to retrieve details of the authenticated user's account.
Get Configuration StatusTool to retrieve the current configuration status of the Codacy system.
Get HealthTool to check the health status of the Codacy API.
Get Organizations Repositories Settings LanguagesTool to get the list of all languages with their extensions and enabled status for a repository.
Get Tool PatternTool to retrieve the definition of a specific pattern for a given tool.
Get User OrganizationsRetrieves all organizations the authenticated user belongs to for a specific Git provider.
Get VersionTool to retrieve the version of the Codacy installation.
List Analysis Organizations RepositoriesTool to list organization repositories with analysis information for the authenticated user.
List Duplication ToolsTool to retrieve the list of duplication detection tools available in Codacy.
List Languages and ToolsTool to retrieve the list of languages supported by available tools.
List Login IntegrationsTool to list configured login providers on Codacy's platform.
List Metrics ToolsTool to retrieve the list of metrics tools available in Codacy.
List ProjectsTool to list all projects accessible to the authenticated user.
List Provider IntegrationsTool to list provider integrations existing on Codacy's platform.
List ToolsTool to retrieve the list of analysis tools available in Codacy.
List Tools PatternsTool to retrieve the list of patterns for a specific tool.

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 this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

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

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_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 your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Codacy functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Codacy tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Codacy
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['codacy']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Codacy tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Codacy tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "codacy-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Codacy MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Codacy tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Codacy related question or task to the agent.\n")

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

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

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Codacy and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['codacy']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "codacy-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Codacy related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've successfully built a LangChain agent that can interact with Codacy through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

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FAQ

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

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

Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain 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 Codacy tools.

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

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

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