Complexity in construction only continues to rise driven by environmental constraints, regulatory compliance, supply chain costs, and talent gaps. To stay competitive, designers and builders must navigate this complexity strategically, and increasingly, they are turning to agentic AI.
AI agents are systems that can not only reason and synthesize information – like we often see with assistants like ChatGPT or Gemini – but these systems can also make decisions and execute on the outcomes that we define for them.
Agents can connect to different data and tools using Model Context Protocol (MCP), which is a big leap forward in what’s possible with AI in construction. This blog explores what MCP servers are, why they matter, and how they can reshape construction workflows.
You can also watch or listen to our recent podcast to learn even more about MCP servers in construction:
What is MCP?
MCP is an open standard created and published by Anthropic for a large language model (LLM) to communicate with external tools, APIs, and data sources. Think of it as an API for a large language model to discover and connect to data sources and tools.
Before MCP, AI capabilities were contained within individual tools or standalone assistants. With MCP, the agent can connect across multiple data sources and tools. Users can define an outcome as well as provide context on how to execute. This enables the agent to connect data across systems and databases. MCP expands the ecosystem of tools available for agents and speeds up their execution.
MCP server components
An MCP server includes three main components:
- MCP host – The environment or platform that runs and manages the AI agents doing the actual work (e.g., Revit, Autodesk Forma, Autodesk Build). The LLM (the “brain”) lives inside the host.
- MCP client – The interface where users or AI agents send requests on behalf of the LLM and receive data from connected tools.
- MCP server – This is the external tool or data source (e.g., your file system, a GitHub repository, or a database) that is made discoverable for the LLM using a standard protocol.
How APIs and MCPs work together
If you’ve ever worked with traditional APIs, then you know that they’re powerful. That being said, APIs require you to hard-code integrations at development time—every connection is permanently baked into your application until you redeploy. If you want to add a new service or change an integration, you need to write code, test, and ship updates.
On the other hand, the LLM using an MCP makes runtime integrations and configurations. The AI discovers available tools when it runs, so you can add, remove, or swap services needed to complete a workflow, all without changing code or redeploying.
In the agentic world, developers will build MCP servers on APIs, making APIs understandable for LLMs. What’s different now is that with those MCP servers, many more people can more easily build custom workflows and automations.
| Traditional APIs | MCPs (Model Context Protocols) | |
| Ease of use | Developer-driven; requires scripting, coding, and in-depth documentation. | Accessible to more people —AI agents can query data or trigger actions using natural language. |
| Speed and scalability | Limited by what they’re programmed to do; expanding functionality requires new code and time. | Dynamic and adaptive; AI agents can discover new capabilities within connected tools as they evolve. |
| Context sharing | Only shares what’s explicitly requested; lacks awareness of broader relationships. | Shares full project context—like model data, file relationships, and user intent—for more intelligent decisions. |
| Maintenance | Needs frequent updates when systems change or APIs break. | Uses a shared protocol that connects to LLMs that adapt automatically to different tools and environments, reducing upkeep. |
Why MCP servers matter in construction
Construction teams deal with more data and digital tools than ever but connecting all of that can be challenging. MCP servers simplify how people and software interact, breaking down barriers between platforms and unlocking true AI-powered collaboration. Consider the following:
Accessibility
MCP servers make advanced technology more approachable. Instead of needing deep technical know-how or coding skills, teams can interact with complex systems through natural language. This levels the playing field and speeds up adoption.
Automation
By connecting AI agents to the right data, MCP servers enable automated workflows like BIM validation, schedule optimization, and defect detection. Tasks that once took hours of manual coordination can now run automatically in the background.
Innovation
Partnering with agents, we can experiment and iterate faster. We are no longer constrained by the cost of making a change (people’s time). Considering the impact of alternative options, like material or design choices for sustainability, can now be done in minutes instead of days. This expands our ability to experiment and make the outcome better.
Real world use cases
When leveraged properly, MCP servers can transform how construction teams work. Here are some potential opportunities that can be built. Consider this as a starting point for your teams to begin exploring how they can save time and be more efficient using agentic AI.
BIM validation
Instead of spending hours manually checking models, AI agents can connect to BIM data and scan for invalid elements or clashes in real time and recommend alternatives. The engineer still needs to review the agent’s recommendations, but they are freed from having to find the clashes manually and clean them up.
Design optimization
With the right data context, AI can suggest smarter design tweaks—like rotating a building 30° for better daylighting or adjusting material choices for cost savings. MCP servers make these insights possible by bridging modeling tools and performance analytics.
Field quality management
Agents can analyze photos or sensor data from the field to detect installation errors early. They can even generate punch lists and documentation automatically, freeing up superintendents to focus on higher-value work.
Spec book automation
Generating spec books and takeoffs manually is tedious. MCP servers let agents pull structured data directly from drawings and models, cutting hours of documentation time.
Getting started with MCP servers
Ready to see what MCP servers can actually do? The good news is, you don’t need to be a developer to start experimenting. Whether you’re curious about automating BIM tasks or integrating AI into your project workflows, you can begin small and build up gradually
Experiment with agentic tools
Start by playing with tools that already support MCPs, like Claude Desktop, ChatGPT, or Cursor. These platforms make it easy to connect to locally hosted MCP servers and test how AI agents perform tasks such as model analysis, data validation, and content generation. Think of it as sandbox mode for AI-driven construction workflows.
If you want to go hands-on, try creating a basic Node.js MCP server. For example, Autodesk’s “Count R’s” tutorial walks you through setting up a simple local server that counts how many times the letter “R” appears in a string. It’s a light, approachable way to learn how AI agents recognize tasks they can’t solve on their own and delegate them to your server.
Explore MCP marketplaces
Explore MCP marketplaces that make it easy to find, connect, and deploy third-party tools built for AI-powered construction workflows. At Autodesk, we are building MCP servers into our portfolio. Built specifically for design and make agent workflows, Autodesk MCP servers (coming soon) can streamline workflows and help you extract more value from your digital tools.
Build toward complex workflows
Start simple and then scale. As you get comfortable, use AI prompts to trigger multi-step tasks: pulling model data, running design checks, or generating reports automatically. Over time, you’ll build an MCP-powered ecosystem that feels less like managing software and more like collaborating with a digital project assistant.
What’s next for MCPs and Autodesk
Autodesk isn’t slowing down. We’re moving into an AI-powered future, with MCP servers facilitating everything from intelligent design to automated construction insights. Here’s what’s ahead for upcoming developments:
Integration with Autodesk Assistant
Autodesk Assistant will soon run on MCP infrastructure. This means users in Revit, AutoCAD, Civil 3D, and Autodesk Construction Cloud can rely on a consistent and context-aware experience across tools. In the future, users could collaborate with Autodesk Assistant to do everything from setting up projects to checks against specifications and standards, automated model updates, and much more. Furthermore, multiple Assistants will be able to “talk” to each other, sharing project context across applications and disciplines.
Public standalone MCP servers
Autodesk is also building public MCP servers, starting with – Revit, Model Data Explorer, and Fusion Data. In the future, we envision a design and make marketplace with Autodesk public MCP servers and third-party MCP servers. Think of it as an app marketplace for AI-powered automation. You can find solutions and then deploy trusted, compliant, and ready-to-integrate MCP endpoints without heavy custom development.
The future of construction workflows
MCP servers and AI agents are setting the stage for a future where every workflow becomes faster, smarter, and more connected.
Smarter, more sustainable design and build
MCP-enabled AI agents can analyze materials, carbon output, and construction methods in real time. This helps teams experiment more and deliver better outcomes without slowing project progress. Whether it’s selecting low-impact materials or optimizing building orientation for energy efficiency, design decisions will be guided by instant, data-driven insight.
AI becomes part of the crew
AI agents won’t feel like “add-ons”. Instead, they’ll be built right into how people work. Designers, estimators, and field teams will collaborate with AI assistants that validate models, generate schedules, flag risks, and even handle documentation behind the scenes. These tools will quietly eliminate friction and free up time for creative problem-solving. And our job will evolve to define outcomes for the agents and provide them with the right context and resources to be successful. This is a new way of collaboration that will power more experimentation, more innovation, and better outcomes.
Augmented and multimodal workflows
The jobsite of tomorrow will blend augmented reality (AR), voice, and visual interfaces powered by MCP servers. Imagine viewing a model overlay through AR glasses and asking, “Show me what’s behind this wall,” or “Flag all missing components.” The MCP framework connects that command to real-time data and executes it instantly.
Collaboration without boundaries
MCPs will unify project data across design, engineering, and construction platforms to create shared digital environments from where everyone works. No more data silos or double handling, just seamless collaboration across teams and tools.
The future of construction is being built right now. MCP servers are quietly connecting the dots between tools, data, and teams. And agentic AI offers an entirely new approach to tackle the increasing complexity of construction. Companies that experiment today will shape how the industry works tomorrow.
Ready to uplevel your AI construction workflows? See how Autodesk MCP servers can help you simplify workflows, improve collaboration, and build smarter.
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