Your users are starting to access your product through AI agents and tools—not just your UI. This engagement designs for that reality: clear tool boundaries, structured content that LLMs can retrieve accurately, and trust patterns that keep humans informed and in control. Built on the Model Context Protocol (MCP) standard and real-world tool-use patterns.
Who it's for
Teams building products that AI agents and tools will access—via MCP servers, tool-use APIs, or retrieval systems. You need to design for a user that isn't human, while keeping humans informed and in control.
Problems this solves
- Your product works through a UI, but users increasingly reach it via AI assistants and agents—and the experience breaks.
- Tool boundaries are unclear: what the agent can do, what requires human confirmation, and what should be refused.
- Content and data structures weren't designed for retrieval by LLMs—context is lost or misrepresented.
- Trust and consent patterns for agent-mediated actions don't exist in your product yet.
What you get
- Tool boundary definitions: what the agent can read, write, and execute—with explicit scope and consent requirements.
- MCP-aware interaction models that map your product capabilities to tool descriptions, input schemas, and resource endpoints.
- Trust and transparency patterns so human users understand what the agent did, why, and what to verify.
- Retrieval-ready content structures that preserve context and accuracy when accessed by LLMs.
How it works
Typically 3–5 weeks. Starts with a capability mapping (what your product does that an agent would want to access), defines tool boundaries and consent flows, and produces interaction models and content structure recommendations. Includes working prototypes for MCP server definitions or tool-use patterns when that accelerates validation.
Proof
Frequently asked questions
- What is MCP?
- The Model Context Protocol is an open standard (created by Anthropic) that standardizes how applications provide context and tool access to LLMs. It defines a structured way for AI agents to discover, read, and act on your product capabilities.
- Do you build MCP servers?
- I design the tool definitions, resource schemas, and interaction models. I can build working MCP server prototypes to validate the design. For production implementation, I work alongside your engineering team.
- Is this relevant if we don't use AI yet?
- If your users or partners will access your product via AI tools in the near future, designing for that access pattern now avoids retrofitting later. The same principles—clear tool boundaries, structured content, explicit consent—improve your product even without AI.
- How do you handle safety and trust?
- Every tool boundary includes explicit consent requirements, scope limitations, and human-in-the-loop checkpoints for high-impact actions. The design principle is: agents should be useful, but humans should always know what happened and why.
Start a conversation
Describe your constraints and the decision you need to make. I'll tell you if I can help.