Mastery Gate: Deterministic Control of AI Agents via WebMCP

Repositorymiskaone/learn-powerplatform-fyiDemo

Mastery Gate is an experimental PL-400 learning platform demonstrating how websites can deterministically govern AI agents via the WebMCP protocol. Instead of relying on prompt instructions, a TypeScript engine dynamically manages tool availability and structurally protects answer keys.

Key points

  1. Bring your own agent: The site operates entirely client-side without hosting models, calling LLM APIs, or storing API keys, relying directly on the visitor's agent runtime.
  2. Structural redaction: Tool response schemas deliberately omit answer keys, preventing the agent from leaking solutions via prompt injection.
  3. Dynamic tool registration: Capabilities such as 'advance_module' are only registered once mastery thresholds are met, and coaching tools are revoked during exams.
  4. Deterministic engine: Grading, rubric evaluation, and routing logic are handled entirely by a unit-tested TypeScript engine, leaving only explanations and Socratic dialogue to the agent.
  5. WebMCP support: Verified working in the ChatGPT desktop app and Chrome 152+ (with testing flags), while Edge Copilot and the Codex Chrome extension currently lack tool-invocation bridges.

Why it matters

Rather than relying on vulnerable prompt-level constraints, Mastery Gate implements guardrails directly in the tool schema and application logic. It illustrates how web applications can safely integrate third-party browser agents without ceding authority or exposing sensitive data.

Analysis

Created for the OpenAI WebMCP Challenge, the repository represents an early-stage prototype built with Next.js and static export. It is an instructive proof-of-concept for the evolving WebMCP standard, highlighting practical benefits alongside current limitations across different agent host environments.

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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