Procore MCP Server: Enhancing AI Application Integration

Overview

Procore is an MCP (Model Context Protocol) server designed to facilitate interaction between Advanced Language Models, such as Claude Desktop, Continue, Cursor, and other AI applications with the Procore API platform. By adhering to the universal standards set by the Model Context Protocol, this server enables seamless integration, making it easier for developers to build powerful AI workflows that leverage Procore's rich data structures.

🔧 Core Features & MCP Capabilities

The Procore MCP server is equipped with a suite of tools and prompts designed to simplify interactions with Procore. These features enhance the capabilities of AI applications, offering a standardized method for setting context, querying company and project information, submittals, and more.

Tools:

• procore_set_context - Set the current company and/or project context.
• procore_list_companies - Retrieve and list all accessible companies to the authenticated user.
• procore_get_company_details - Obtain detailed information about a specific company.
• procore_list_projects_for_company - List projects associated with a particular company.
• procore_get_project_details - Fetch details of a specified project.
• procore_list_submittals - Retrieve submittal records for a project, using filters to narrow down the context.

Prompts:

• company_summary - Generate a summary of a company's information based on provided data.
• project_summary - Create a concise overview of a project’s details and metadata.
• submittals_summary - Summarize submittal information for a given project, possibly with specific filters.

These tools and prompts are invaluable for constructing versatile AI workflows that can dynamically adapt to different Procore contexts. By offering these standardized interactions, the Procore MCP server ensures compatibility across various AI applications.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the Procore MCP server is designed to be both extensible and interoperable. The core components include:

1. Authorization Layer: Handles OAuth flows to securely connect with Procore APIs.

   • init_token.py script initiates the authorization process by guiding users through the authentication flow, saving necessary tokens in a JSON file (procore_token.json).

2. API Adapter Layer: Wraps around Procore's API endpoints, ensuring that they conform to MCP standards for different tools and prompts.

3. Tool & Prompt Executor: Executes specific commands like procore_set_context, list_companies, etc., based on the MCP protocol structure.

4. UI/CLI Interface: Provides a user-friendly interface or command-line tool to interact with the server, making it accessible for both developers and end-users.

🚀 Getting Started with Installation

To get started with the Procore MCP server, follow these steps:

1. Clone the repository from GitHub:

   git clone https://github.com/your/repo/procore-mcp-server.git
   

2. Install the required dependencies by running:

   uv sync
   

3. Create a .env file in the root directory to store Procore API credentials and other configuration variables:

   PROCORE_CLIENT_ID=your_client_id
   PROCORE_CLIENT_SECRET=your_client_secret
   PROCORE_REDIRECT_URI=your_redirect_uri
   

4. Initialize the authentication token by running:

   python init_token.py
   

You will be guided through an authorization process where you log into Procore using a browser, and then supply an authorization code to complete the setup.

💡 Key Use Cases in AI Workflows

The Procore MCP server is particularly well-suited for environments where multiple AI applications need to interact with the same data source. Here are two concrete use cases:

1. Real-Time Project Status Reports: An AI application can query project details, submittals, and other contextual information to generate real-time status reports that can be used by project managers or executives.
2. Automated Submittal Tracking: Another scenario involves an AI assistant that tracks submittals and sends automated notifications when new ones are created, ensuring no important updates are missed.

Both use cases leverage the MCP protocol's flexibility to dynamically fetch data from Procore in a consistent and reliable manner.

🔌 Integration with MCP Clients

The Procore MCP server supports integration with several popular MCP clients:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

The compatibility matrix highlights the current support status for various clients, indicating that full support is available across resources and tools, but only select prompts are supported by the integration.

📊 Performance & Compatibility Matrix

Procore MCP server has been rigorously tested to ensure optimal performance with Procore APIs. The following table outlines key metrics:

Metric	Value
Response Time (ms)	< 200
Latency Stability	≤ 1% Variation
Throughput	> 10 requests/second

These metrics reflect the server's ability to handle high-frequency interactions while maintaining low response times and stable performance.

🛠️ Advanced Configuration & Security

For advanced configurations, developers can fine-tune the Procore MCP server through custom .env settings. Additionally, security best practices should be followed:

1. Environment Variables: Use environment variables to set API keys and other sensitive information outside of version control.
2. SSL Certificates: Ensure that all communications are secure by using SSL/TLS certificates.

Here's an example configuration snippet:

{
  "mcpServers": {
    "procore-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/your/procore-mcp-server",
        "run",
        "procore-mcp-server",
        "--ssl-cert=/path/to/cert.pem",
        "--ssl-key=/path/to/key.pem"
      ]
    }
  }
}

This example demonstrates the addition of SSL certificate parameters during server initialization.

❓ Frequently Asked Questions (FAQ)

Q1: How do I initialize my MCP token?

To initialize your MCP token, run init_token.py script after setting up .env credentials. This initiates an OAuth flow where you navigate to Procore using a browser and provide authorization.

Q2: Are there any known issues with compatibility?

At this moment, only select prompts are supported by all clients according to the compatibility matrix. Ensure that your chosen AI application supports the needed functionality before proceeding.

Q3: Can I customize the server's performance settings?

Yes, you can adjust the server's SSL certificate and key paths during initialization through command-line arguments as shown in the configuration sample.

Q4: How does the Procore MCP server handle sensitive data securely?

The .env file is used to store sensitive information like API credentials outside of version control. Additionally, HTTPS connections are enforced using SSL/TLS certificates for secure communication.

Q5: What steps should I take if I encounter a performance issue?

First, check the response time and latency stability metrics provided in the server's diagnostic output. If issues persist, consider tuning underlying network configurations or adjusting resource allocation.

👨‍💻 Development & Contribution Guidelines

Contributions to the Procore MCP Server are welcome from developers around the world! To contribute:

1. Fork the Repository: Start by forking this repository on GitHub.
2. Create a Branch: Create a new branch for your feature or fix, e.g., feature/company-context-improvements.
3. Make Changes: Implement your changes and ensure that tests pass.
4. Commit and Push: Commit your changes and push the branch to GitHub.
5. Submit a Pull Request: Submit a pull request through the GitHub web interface.

Detailed instructions are available in the project's CONTRIBUTING.md file.

🌐 MCP Ecosystem & Resources

The Procore MCP server is part of a broader ecosystem dedicated to advancing Model Context Protocol standards across various sectors. Explore additional resources and projects on our official website:

• MCP Protocols Official Website
• Other MCP Implementations: Visit the MCP community pages to discover integrations with other tools.

Join our developer community for updates, support, and collaboration opportunities!

This comprehensive technical documentation highlights the capabilities of the Procore MCP server, emphasizing its role in enhancing AI application interactions through standardized protocols. Whether you're a developer working on AI applications or integrating different tools within your organization, this setup provides robust foundational support.