# How to integrate Appveyor MCP with CrewAI ```json { "title": "How to integrate Appveyor MCP with CrewAI", "toolkit": "Appveyor", "toolkit_slug": "appveyor", "framework": "CrewAI", "framework_slug": "crew-ai", "url": "https://composio.dev/toolkits/appveyor/framework/crew-ai", "markdown_url": "https://composio.dev/toolkits/appveyor/framework/crew-ai.md", "updated_at": "2026-05-12T10:01:52.228Z" } ``` ## Introduction This guide walks you through connecting Appveyor to CrewAI 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 CrewAI 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) - [LangChain](https://composio.dev/toolkits/appveyor/framework/langchain) - [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) ## TL;DR Here's what you'll learn: - Get a Composio API key and configure your Appveyor connection - Set up CrewAI with an MCP enabled agent - Create a Tool Router session or standalone MCP server for Appveyor - Build a conversational loop where your agent can execute Appveyor 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 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 Before starting, make sure you have: - Python 3.9 or higher - A Composio account and API key - A Appveyor 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 Appveyor 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 Appveyor 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 Appveyor **What's happening:** - You create a Appveyor only session through Composio - Composio returns an MCP HTTP URL that exposes Appveyor tools ```python composio_client = Composio(api_key=COMPOSIO_API_KEY) session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["appveyor"]) 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=["appveyor"], ) 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 Appveyor through Composio's Tool Router. The agent can perform Appveyor 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 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) - [LangChain](https://composio.dev/toolkits/appveyor/framework/langchain) - [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) ## Related Toolkits - [Supabase](https://composio.dev/toolkits/supabase) - Supabase is an open-source backend platform offering scalable Postgres databases, authentication, storage, and real-time APIs. It lets developers build modern apps without managing infrastructure. - [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 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 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. --- [See all toolkits](https://composio.dev/toolkits) ยท [Composio docs](https://docs.composio.dev/llms.txt)