Many of the traditional legal issues faced by technology startups also apply to artificial intelligence startups. Although there are some unique challenges related to model-context-protocol developers and startup companies, the foundational requirements remain largely the same. These include establishing the appropriate corporate structure, identifying and protecting intellectual property, creating contracts for contractors, founders, and employees, obtaining patent protection, preparing investor documents, licensing software, drafting website agreements, and creating software-as-a-service agreements. Addressing these key legal issues is best handled by a lawyer who has expertise and experience in artificial intelligence technology.
We represent artificial intelligence companies at every stage of startup and funding, including those developing MCP systems. We, along with our clients, believe that MCP will play a crucial role in the AI landscape, particularly in terms of user adoption and the business implementation of AI systems.
MCP is the Key to AI Adoption and Business Integration
AI now powers everything from customer support to enterprise workflows, but linking it with existing applications is still clunky. Most tools rely on scraping or simulating user clicks, methods that are easily broken and introduce security risks.
The Model Context Protocol (MCP) offers a new standard: direct, structured communication between AI and applications. By removing the guesswork, MCP enables faster, more reliable, and more secure integrations.
For AI startups, the implications go beyond technical execution. Building on MCP requires both engineering excellence and a robust legal foundation that encompasses trademarks, open-source licensing, IP strategy, and corporate structuring. Attorneys who understand AI agents and MCP are becoming critical partners in this space.
What is Model Context Protocol (MCP)?
MCP is a communication standard that allows AI systems to interact directly with websites and applications in a structured manner. Instead of guessing at what’s on the screen or simulating a human click, MCP allows AI a clear path to execute functions.
Think of it as teaching the AI the language of the app itself.
- An e-commerce assistant can run addToCart() instead of trying to “click” a button on a page.
- A CRM integration can execute createLead() directly from a client chat, without parsing messy HTML.
For founders, the benefits are clear: faster, more reliable, and more secure integrations that scale as the company grows. By reducing friction between AI and software, MCP turns fragile workflows into stable pipelines. This is where business users of AI will achieve productivity. On the consumer side, we will see real-world adoption by everyday users as they transition from AI chatbots to true AI assistants.
The Difference Between MCP and AI Agents
In recent quarters, the buzzword has been AI agents. However, once you start experimenting with these agents, you quickly realize that translating the concept into a reliable implementation is challenging. While AI agents can be launched using no-code tools, developing production-ready agents that can perform real tasks still requires someone with coding skills. Often, agents struggle with multi-step tool usage and exercising the discretion needed to achieve specific outcomes.. Most AI agents today behave like “virtual humans.” They click through interfaces, scrape HTML, and simulate how a person might use an app. This approach is fragile; one small change to the UI can break the integration, and it creates unnecessary security risks.
MCP takes a different path. Instead of interpreting frontends, it enables direct, structured calls to application functions. This AI integration model is faster, more reliable, and easier to maintain as products scale. For developers and founders, it reduces technical debt while improving long-term stability.
This doesn’t mean agents disappear. They remain useful for multi-step orchestration, where actions span across multiple systems or workflows. However, many common tasks, such as integrating AI tools for SaaS dashboards, customer onboarding flows, or creating CRM leads, will shift to MCP-enabled calls.
For startups, this shift means rethinking both technical roadmaps and the AI startup legal checklist. Companies building MCP-based tools must plan not only for faster deployment but also for intellectual property protection, open-source licensing decisions, and platform liability considerations.
The “Holy Grail” of User Adoption: A User Talks and AI Does
MCP is another step forward in eliminating the need for code and coders, allowing an end user to achieve a specific outcome. Currently, an AI chatbot can listen to a person speaking and provide an output. We are starting to see the first signs of tool use by artificial intelligence models, which enable a person to speak and have a task performed. However, there’s still a significant amount of code required to make an AI agent reliably execute a task. The long-term vision for MCP is straightforward: a user provides an instruction in plain language, and the AI executes the task instantly.
- A manager says, “Onboard this client and send the contract.”
- The AI interprets intent, calls the right MCP-enabled functions, and completes the workflow.
This is the future of AI tooling, a world where integrations happen natively, without brittle scripts or guesswork. For SaaS companies, e-commerce platforms, and enterprise dashboards, MCP can automate repetitive actions, reduce costs, and deliver a frictionless user experience.
But the risks are real. In 2022, the EEOC investigated an AI hiring platform accused of discrimination under Title VII of the Civil Rights Act. The case highlighted how automation, even when well-intentioned, can expose companies to liability if bias infiltrates decision-making. MCP-driven workflows will face similar scrutiny, especially in hiring, healthcare, and finance. And civil rights laws are just one example of regulatory compliance. If you are engaging in online marketing, you must comply with FTC regulations. Developing, implementing, and analyzing a marketing plan using an AI agent or MCP-enabled browser requires consideration of various consumer protection laws. The further we allow AI to perform tasks, utilize tools, and exercise discretion, the more crucial it becomes to account for legal and regulatory compliance issues. If you are offering your MCP software as a service, ensure that your service agreement reduces risks, includes class-action waivers, and places the burden of legal compliance on the subscriber.
A recent news segment highlights how AI hiring tools are already raising concerns about discrimination in real-world workplaces.
To stay ahead, companies should pair technical innovation with legal foresight by drafting AI contractor agreements with strong IP assignment, securing trademark protection for AI tools and plugins, and selecting the most suitable open-source license for MCP projects prior to launch.
Key Legal Issues for MCP Developers and AI Companies
Building with MCP unlocks powerful opportunities, but it also exposes startups to legal risks that can delay growth or derail investment. Addressing these issues early is essential.
a. Branding and Trademarks
Securing trademark registration for your MCP-based platform or plugin is one of the first steps in protecting your brand. Without it, another company could register a name that is confusingly similar, forcing an expensive rebrand. Clearance searches also help avoid infringement lawsuits that can stall product launches. For AI founders, trademark protection for AI tools and plugins is very foundational.
As with every new technology, we see clients who treat the brand and trademark issues as an afterthought. Think about how many tools are called Co-Pilot. Trademark law will allow only one company to use the Co-Pilot or a variation of Co-Pilot as a brand, and I would expect the threat letters for trademark infringement are already being sent out by Microsoft Corporation as a result of its commitment to the brand Co-Pilot for its AI tools.
b. Open-Source Licensing
Many MCP projects rely on open-source code. Choosing the right license, whether MIT, GPL, AGPL, or a proprietary mode, determines how others can use, modify, or distribute your work. Mixing incompatible licenses can trigger costly disputes. A strong open-source strategy also involves utilizing contributor agreements and implementing clear fork governance. Startups should ask directly: What is the best open-source license for MCP projects to balance community growth with IP control?
Many AI projects are open source. Understanding GPL, AGPL, MIT, and other licenses is important at the very beginning of your startup effort. Ensuring that your business model and monetization strategy account for open-source license requirements and limitations is mission-critical.
c. Corporate Structure and Investment Readiness
Investors will expect a clean corporate structure before committing capital. Forming the right entity, typically a C-Corp for venture financing, and defining equity allocations for founders, employees, and advisors is critical. These steps should be at the top of every AI startup’s legal checklist; without them, fundraising and exit opportunities can be compromised.
d. Employment and Contractor Agreements
AI startups often rely on contractors or distributed teams. Without airtight contracts, ownership of MCP-related IP can remain unclear. Every agreement should include:
- IP assignment clauses to ensure the company, not the contractor, owns the code.
- Confidentiality and restrictive covenants to prevent leaks of sensitive AI tool intellectual property.
Comprehensive AI contractor agreements protect your most valuable asset: the innovation itself.
e. IP Audits and Protection Strategy
As products evolve, so does the IP portfolio. Founders should conduct regular IP audits to catalog proprietary code, datasets, trademarks, and trade secrets. Patent filings may be appropriate for novel algorithms or integration methods. Protecting MCP integration logic from unauthorized use strengthens both valuation and licensing opportunities. A proactive IP protection strategy for AI companies makes the difference between leading the market and losing hard-won advantages.
f. Platform and Plugin Liability
Offering MCP-based plugins or integrations introduces exposure to misuse. Drafting clear Terms of Service can limit liability when third-party AIs or clients misuse your tool. Cases under laws like BIPA (Biometric Information Privacy Act) and ADA (Americans with Disabilities Act) demonstrate how quickly liability can escalate. By managing MCP legal issues at the platform level, companies safeguard both reputation and runway.
Moving Forward with MCP
MCP is a technical upgrade and a standard set to redefine how AI connects with the software ecosystem. The upside is clear: faster integrations, reduced errors, and scalable tools. The risks, however, are equally significant.
Teams building on MCP must address legal considerations for MCP developers early. It means selecting the right open-source licensing for AI tools, structuring the company for investment, securing AI platform trademark protection, and implementing a clear IP protection strategy for AI integrations. Failing to follow these steps exposes both the product and the business to unnecessary risk.
Traverse Legal helps AI companies design legal strategies aligned with their technical ambitions. From trademark registrations to corporate structuring and open-source governance, our attorneys understand the realities of MCP and the legal frameworks surrounding it.
Book a consultation with Traverse Legal to secure your MCP roadmap with the right protections.
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