Modern applications increasingly depend on AI collaboration and external integrations, but security and user trust are paramount. The Model Context Protocol (MCP) introduces client components that transform how apps interact with AI and servers allows users to stay in control. Understanding these clients unlocks safer, smarter, and more adaptive user experiences.
Key Features of MCP Clients
MCP clients aren’t just intermediaries; they offer advanced features that elevate collaboration. Embedded within host applications, these protocol-level components shape secure and transparent interactions between users, AI models, and external servers. Notable capabilities include sampling, filesystem roots, and elicitation.
Sampling: User-Driven AI Model Access
Sampling empowers servers to request language model outputs via the client, enabling agentic behaviors, like data analysis or recommendation generation, without direct model integration. The client ensures users remain in charge: every sampling request requires explicit approval, and users can adjust prompts or responses before the server sees them.
- Approval controls: Sampling requests only proceed with user consent, allowing modifications or denials as needed.
- Transparency: Clients clearly present prompts, model choices, and token limits for each request.
- Customizable security: Users can configure model selection, auto-approval preferences, and context sharing. Sampling is sandboxed from the main conversation to protect user privacy.
Real-World Example: Flight Analysis
Consider a travel booking app analyzing flight options. The server asks the client to run an AI-driven comparison that incorporates user preferences like layovers and departure times. Users review and approve the request, then inspect or adjust recommendations before results reach the server ensuring transparency and trust.
Roots: Controlled Filesystem Access
Roots define exactly which directories servers can access. Rather than granting blanket permissions, roots use file:// URIs to specify folders for reading, writing, or searching. This ensures that only authorized data is available, and the client always mediates access.
- Dynamic updates: As users open or close projects, roots are updated and servers are notified of changes.
- Granular control: Only files within declared roots are accessible, with all actions subject to the client’s security policies.
Real-World Example: Travel Planning Workspace
A travel agent’s workspace might include separate roots for itineraries, templates, and client files. If a new folder is accessed, the client updates roots so servers only interact with the relevant directory protecting all unrelated data.
Elicitation: Structured, Safe Data Collection
Elicitation lets servers request user input in a structured, secure way. Instead of assuming unknown details or risking failures, servers can prompt users for specific information, like booking confirmations or preferences, via rich UI elements and client-validated schemas.
- Context-rich requests: Users see which server is requesting what data, why it’s needed, and how it will be used.
- Flexible responses: Users may accept, partially complete, or decline requests; the client validates submissions before passing them along.
- Privacy assurance: Sensitive fields (like passwords) are never solicited, and users review all data before sharing.
Real-World Example: Holiday Booking Approval
When booking a holiday, the server uses elicitation to gather necessary details such as room preferences or travel insurance through validated prompts. If additional information is required for travelers, only relevant fields are requested, streamlining the experience while maintaining privacy.
Advancing Secure, User-Centric AI
MCP clients are redefining how applications mediate between users, AI, and external services. By combining sampling, roots, and elicitation, they deliver adaptive, secure, and transparent workflows. The result: users stay empowered, security is enforced, and AI collaboration reaches its full potential.
How Model Context Protocol Clients Securely Empower AI-Driven Workflows