Model Context Protocols (MCPs) are quickly becoming a key technology that helps AI systems and applications communicate with each other in a clear, structured, and reliable way. For startups building products around MCPs, this innovation opens big opportunities, but also introduces a new set of legal, regulatory, and business challenges. This article explores what MCP startups are, the main legal risks they face, and how good legal guidance can help them grow safely and sustainably.
What is an MCP Startup ?
In simple terms, MCP (Model Context Protocol) is a new standard that lets AI tools “talk” to apps through structured connections, rather than through messy or unreliable methods like copying what’s on a screen. Think of it as the USB-C for AI, a universal connector that lets different systems work together smoothly.
A startup working with MCP technology might be:
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Building the underlying MCP framework,
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Offering MCP-based services, or
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Integrating MCP capabilities into existing platforms.
These companies are positioning themselves at a crucial layer of the AI ecosystem, making them central to how future digital systems will interact.
But along with this opportunity comes legal complexity. MCP startups operate across several domains (software, data, and artificial intelligence) which means they must manage:
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Intellectual property (who owns the code, data, and innovations),
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Contracts and licensing,
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Data protection and AI regulations,
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Liability risks, and
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Investor and compliance requirements.
Understanding and addressing these early helps MCP startups avoid costly mistakes, build trust with partners and users and prepare for long-term success.
Key Legal Risks & Issues for MCP Startups
Intellectual Property & Ownership
a) Chain of Title & Assignment
Since MCP startups often rely on code, APIs, model connectors, and datasets, ensuring unbroken assignment of all contributions is essential. Contractors, open-source modules, and third-party contributors must sign forward-looking assignment (or work-for-hire) agreements. Failure to secure assignment may jeopardize freedom to operate or block exit transactions.
b) Licensing & Open-Source Exposure
An MCP startup likely will integrate open-source libraries or frameworks. Some open-source licenses (e.g. GPL, AGPL) impose obligations incompatible with proprietary use, which may threaten core proprietary layers. Startups must audit dependencies carefully and establish a license governance policy.
c) Patent Strategy vs Trade Secrets
Some MCP logic (e.g. routing, context management, API orchestration) may be patentable in some jurisdictions, while other elements are better kept as trade secrets (e.g. tuning methods, hyperparameters). Deciding which parts to patent and which to keep in secret must align with the business model (licensing, defensive posture, openness).
d) Trademark & Branding
Because MCP-based services may become key connectors across AI ecosystems, brand identity and trust carry weight. Registering trademarks at the appropriate levels (national, EU, international) early helps prevent downstream conflicts.
e) Data / Model Ownership & Rights
Many MCP startups will mediate, transform, or route data. Questions arise whether outputs, embeddings, or derived models are owned by the platform, by clients, or by third-party AI providers. Explicit contractual clarity is essential.
Contracts & Commercial Agreements
a) Client / Platform Agreements & Terms of Use
Your agreements must define scope of use, permissible APIs, SLA commitments, indemnities, and termination rights. Given that downstream clients may misuse the MCP interface or chain calls across systems, limitations and liability caps are critical.
b) Developer / Partner / Plugin Agreements
It is typical in MCP ecosystems to allow third parties to build “plugins” or “connectors” to your protocol. Contracts must define interface standards, versioning, revenue share, liability allocation, update obligations, and exit handling.
c) IP License Grants & Restrictions
You may grant clients or partners licenses (e.g. limited use, node-limited, white-label) over your MCP technology or connector modules. Licenses must align with enforcement strategy and guard against misuse or leakage.
d) Data Licensing / Usage Rights
If your MCP system processes proprietary datasets, you must ensure you have rights (or licenses) from data providers. If you federate or aggregate data, your agreements should address data usage, retention, anonymization, sub-licensing, revocation and derivative works.
Regulatory / Compliance Risks
a) Data Protection & Privacy
In many jurisdictions, MCP systems may process personal data, perhaps moving data across systems in real time. Ensuring compliance with GDPR (EU), CCPA (US), or other regimes is paramount. You must design privacy by default, minimize data retention, support rights (access, deletion) and ensure lawful basis for transfers.
b) AI Regulation & Standards
Governments are actively debating regulation of AI models, auditability, transparency, and liability. MCP startups should monitor rulemaking (e.g. EU AI Act, U.S. AI bills) and anticipate compliance obligations such as explainability, risk classification or mandated audits.
c) Cybersecurity & Incident Response
Because MCP is a connective layer, vulnerabilities may cascade. You must adopt robust security measures, incident response plans, breach notification and contractual rights to audit / enforce security obligations.
Liability & Indemnification
a) Upstream / Downstream Liability
If your MCP interface triggers a harmful or illegal action (e.g. deletes data, executes unauthorized commands), your exposure may span multiple layers. Proper indemnities, liability caps and disclaimers are indispensable.
b) Third-Party Claims & Infringement
Because MCP may orchestrate third-party components or models, risk of infringing patents or models is non-trivial. Build indemnification protection, insurance and clearance processes.
c) Auditability, Traceability & Forensics
To defend against claims, your architecture should support logging, versioning, and audit trails. In particular, forensics may rely on MCP’s design choices to help or hinder traceability. (Notably, academic work is already studying how MCP may support reproducible audit in forensic scenarios).
Strategic Legal Design: Best Practices & Counsel Role
To succeed, MCP startups must embed legal design into their architecture from Day 0. Below are recommended practices and areas where legal counsel becomes a strategic partner:
Early-Stage Architecture & Legal Co-Design
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Legal-architecture alignment: Before coding, legal input should help define module boundaries, data flows, API constraints, and plugin models, so that compliance, traceability, and contractual mapping are built in.
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Governance & Versioning Planning: Define versioning protocols, deprecation strategies, and upgrade rights, as legal risk arises when backward compatibility is broken.
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Access & Permissions Controls: Use role-based permissions and least-privilege models to limit misuse and chain-of-blame complexity.
IP & Open Governance
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Establish a license review board: For every external dependency or third-party module, counsel should assess license implications.
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Gate contributions: Use a contribution agreement and a legal review process for third-party contributions to your code or connectors.
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Selective openness: Decide which parts of your MCP stack to publish (for interoperability) and which to keep proprietary—balancing community adoption and defensibility.
Contractual Framework & Risk Allocation
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Template layering: Use master agreements (platform-wide) plus annexes / schedules for individual client or plugin relationships, thereby enabling flexible carve-outs and term variations.
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Liability baskets & caps: Set aggregate liability limits and carve out gross-negligence or willful misconduct.
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Audit & compliance rights: Include rights to audit client or plugin compliance, run security reviews, and require remedial action.
Compliance, Monitoring & Insurance
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Regulatory monitoring: Maintain an internal legal or compliance team (or outsource) to monitor evolving AI / data laws and adjust contract templates and operations accordingly.
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Data protection by design: Conduct Data Protection Impact Assessments (DPIAs) for sensitive use cases; embed consent or anonymization where needed.
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Insurance coverage: Acquire errors & omissions (E&O) and cyber insurance tailored to AI / software risk. Negotiate favorable indemnity from partners.
Due Diligence & Investor Readiness
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IP diligence toolkits: Maintain a clear register of assignments, open-source audits, and third-party rights—this becomes a key diligence topic for investors or acquirers.
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Documentation & workflows: Capture internal architecture, logs, versioning, risk assessments, and incident reports to reassure diligence teams.
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Exit strategy alignment: Design contracts, ownership, and sublicensing so that a future acquirer or public offering won’t be hamstrung by legacy obligations.
European & Greek Legal Context: EU AI Act, GDPR and National Layers
Concluding Reflections & Outlook
MCP startups occupy a pivotal position at the intersection of AI infrastructure and application ecosystems. The technical promise is healthy adoption, modular growth, and interconnectivity. The legal challenge is that many existing doctrines of IP, liability, data regulation, are only beginning to catch up with the realities of AI orchestration layers.
A law firm like Tsamichas, combining deep technology sensitivity with legal sophistication, can guide MCP startups to:
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Build defensible IP stacks
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Draft airtight contractual frameworks
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Proactively comply with data / AI regulation
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Design architectures with liability auditability
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Prepare for scalable financing, M&A or exit
In doing so, MCP startups can reduce legal friction, enhance valuation and position themselves as foundational infrastructure providers in the next wave of AI systems.
