# How to integrate Appveyor MCP with LangChain

```json
{
  "title": "How to integrate Appveyor MCP with LangChain",
  "toolkit": "Appveyor",
  "toolkit_slug": "appveyor",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/appveyor/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/appveyor/framework/langchain.md",
  "updated_at": "2026-05-12T10:01:52.228Z"
}
```

## Introduction

This guide walks you through connecting Appveyor to LangChain using the Composio tool router. By the end, you'll have a working Appveyor agent that can list all projects in your appveyor account, get build artifacts for the latest job, show all deployment environments available through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Appveyor account through Composio's Appveyor MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Appveyor with

- [OpenAI Agents SDK](https://composio.dev/toolkits/appveyor/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/appveyor/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/appveyor/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/appveyor/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/appveyor/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/appveyor/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/appveyor/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/appveyor/framework/cli)
- [Google ADK](https://composio.dev/toolkits/appveyor/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/appveyor/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/appveyor/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/appveyor/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/appveyor/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Connect your Appveyor project to Composio
- Create a Tool Router MCP session for Appveyor
- Initialize an MCP client and retrieve Appveyor tools
- Build a LangChain agent that can interact with Appveyor
- 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 Appveyor MCP server, and what's possible with it?

The Appveyor MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Appveyor account. It provides structured and secure access to your CI/CD pipelines, so your agent can perform actions like managing builds, retrieving artifacts, listing projects, and overseeing users and roles on your behalf.
- Project and build discovery: Quickly list all your Appveyor projects and their build jobs, making it easy for your agent to monitor or summarize your CI activity.
- Artifact retrieval: Direct your agent to fetch build artifacts after a job completes, so you can automate download or inspection workflows without manual intervention.
- Deployment environment overview: Ask your agent to enumerate all available deployment environments, helping you plan and automate deployment tasks with up-to-date environment data.
- User and team management: Effortlessly list all users in your Appveyor account, making it simple to audit team membership or automate notifications and permissions.
- Role inspection and permission management: Let your agent retrieve and review roles and their details, enabling automated governance checks and streamlined access control workflows.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `APPVEYOR_DELETE_BUILD` | Delete Build | Tool to delete a build by ID. Use when you need to remove a build from AppVeyor. The API returns 204 No Content on successful deletion. |
| `APPVEYOR_DOWNLOAD_BUILD_LOG` | Download Build Log | Tool to download the build log for a specific job. Use when you need to retrieve the log output from a completed or running build job. |
| `APPVEYOR_GET_BUILD_ARTIFACTS` | Get Build Artifacts | Tool to get the list of artifacts for a specific build job. Use when you need to retrieve artifacts after a job completes. |
| `APPVEYOR_GET_BUILD_BY_VERSION` | Get Build By Version | Tool to get a specific project build by version number. Use when you need to retrieve detailed information about a build using its version identifier. |
| `APPVEYOR_GET_ENVIRONMENTS` | Get Environments | Tool to get a list of all deployment environments. Use when you need to enumerate available environments before creating deployments. |
| `APPVEYOR_GET_PROJECT_BRANCH_STATUS_BADGE` | Get Project Branch Status Badge | Tool to get a project branch status badge image. Returns a PNG or SVG badge image showing the build status for a specific project branch. |
| `APPVEYOR_GET_PROJECTS` | Get Projects | Tool to get a list of all projects for the authenticated account. Use after authentication to enumerate available projects. |
| `APPVEYOR_GET_PROJECT_STATUS_BADGE` | Get Project Status Badge | Tool to get project status badge image. Use when you need to retrieve the status badge for displaying project build status. |
| `APPVEYOR_GET_PUBLIC_PROJECT_STATUS_BADGE` | Get Public Project Status Badge | Tool to get status badge image for a project with a public repository. Use when you need to retrieve a build status badge for display or documentation purposes. |
| `APPVEYOR_GET_ROLE` | Get Role | Tool to retrieve details of a specific role. Use when you need to inspect permissions and metadata of a role by ID. |
| `APPVEYOR_GET_ROLES` | Get Roles | Tool to retrieve all roles in the account. Use when you need to enumerate available roles before assigning permissions. |
| `APPVEYOR_GET_USER_INVITATIONS` | Get User Invitations | Tool to retrieve all pending user invitations in the account. Use when you need to list all outstanding invitations sent to potential team members. |
| `APPVEYOR_GET_USERS` | Get Users | Tool to retrieve all users in the account. Use when you need to list all team users in your AppVeyor account. |
| `APPVEYOR_LIST_COLLABORATORS` | List Collaborators | Tool to retrieve all collaborators in the account. Use when you need to list all team collaborators in your AppVeyor account. |

## Supported Triggers

None listed.

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

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

No description provided.

### 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

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
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()
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
```

### 5. Initialize Composio client

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 Appveyor tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
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")
```

```typescript
const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Appveyor 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 Appveyor tools as needed
```python
# Create Tool Router session for Appveyor
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['appveyor']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['appveyor']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "appveyor-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)
```

```typescript
const client = new MultiServerMCPClient({
    "appveyor-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Appveyor 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")
```

```typescript
let conversationHistory: any[] = [];

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

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
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=['appveyor']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "appveyor-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 Appveyor 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())
```

```typescript
import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['appveyor']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "appveyor-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Appveyor related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Conclusion

You've successfully built a LangChain agent that can interact with Appveyor 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.

## How to build Appveyor MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/appveyor/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/appveyor/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/appveyor/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/appveyor/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/appveyor/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/appveyor/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/appveyor/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/appveyor/framework/cli)
- [Google ADK](https://composio.dev/toolkits/appveyor/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/appveyor/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/appveyor/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/appveyor/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/appveyor/framework/crew-ai)

## Related Toolkits

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- [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.
- [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.
- [Abuselpdb](https://composio.dev/toolkits/abuselpdb) - Abuselpdb is a central database for reporting and checking IPs linked to malicious online activity. Use it to quickly identify and report suspicious or abusive IP addresses.
- [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.
- [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.
- [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.
- [Better stack](https://composio.dev/toolkits/better_stack) - Better Stack is a monitoring, logging, and incident management solution for apps and services. It helps teams ensure application reliability and performance with real-time insights.
- [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews.
- [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease.
- [Blocknative](https://composio.dev/toolkits/blocknative) - Blocknative delivers real-time mempool monitoring and transaction management for public blockchains. Instantly track pending transactions and optimize blockchain interactions with live data.
- [Bolt iot](https://composio.dev/toolkits/bolt_iot) - Bolt IoT is a platform for building and managing IoT projects with cloud-based device control and monitoring. It makes connecting sensors and actuators to the internet seamless for automation and data insights.

## Frequently Asked Questions

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

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

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

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

---
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