HyperXosist-Agent: Feedback-to-Fix MCP

A Remote MCP workflow that filters noisy product feedback and converts actionable signals into structured engineering handoffs for Codex.

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Inspiration

Product feedback on social platforms is valuable, but it is fragmented, repetitive, and noisy. AI agents can search for it, yet the results rarely arrive in a form that developers can immediately use to improve a product.

HyperXosist-Agent was built to close that gap: it turns noisy public feedback into a structured Feedback-to-Fix workflow for AI agents and developers.

What it does

HyperXosist-Agent provides a publicly available Remote MCP service that helps AI agents:

  • plan targeted product-feedback research
  • generate noise-reduced official X search URLs
  • filter weak or irrelevant signals
  • preserve high-value feedback as KEEP-only evidence
  • produce structured handoffs for downstream coding workflows
  • separate free planning from paid production execution through x402

Rather than scraping X directly, the system creates structured research missions and official search URLs that can be used by humans, agents, or external collection systems.

Demo scope

This submission uses synthetic feedback for demonstration. The public GitHub Pages site and Remote MCP endpoint are real and reproducible. The demo does not directly scrape X.

Example synthetic feedback:

{
  "text": "Users cannot understand the onboarding steps.",
  "source": "synthetic demo feedback",
  "url": "https://example.com/feedback/1"
}

The workflow calls:

  • hyperxosist_search_plan
  • hyperxosist_filter_signals
  • hyperxosist_build_handoff

Why it matters

Teams often collect feedback but fail to convert it into engineering action. HyperXosist-Agent focuses on the missing middle layer between discovery and implementation.

The system is designed for independent developers, AI-agent builders, product teams, support teams, researchers, and developers using Codex for implementation.

The goal is not merely to search X. The goal is to convert public signals into a repeatable path from feedback discovery to code change.

How it works

  1. A user or AI agent defines a product, problem, or feedback target.
  2. The Remote MCP generates a search plan and noise-reduced official X search URLs.
  3. Filtering tools distinguish actionable signals from weak or duplicated content.
  4. A Signal-to-Fix handoff packages the evidence for a downstream engineering agent.
  5. Codex can use that structured handoff to inspect a repository, plan a fix, implement changes, and verify the result.
  6. Free MCP planning tools remain separate from automated production execution, which is protected by an x402 payment boundary.

Key features

  • Remote Streamable HTTP MCP
  • official MCP Registry listing
  • multilingual search planning
  • noise-reduced X query generation
  • signal filtering
  • structured Signal-to-Fix handoff
  • public human-facing demo
  • x402-paid production execution using USDC on Base
  • shared core across Remote MCP, CLI, npm, and stdio MCP interfaces

How Codex and GPT-5.6 were used

Codex and GPT-5.6 were used throughout the project to:

  • analyze the existing architecture
  • separate the public demo, Remote MCP, and paid API boundaries
  • implement and refine MCP tools
  • improve metadata and discovery paths
  • validate Streamable HTTP behavior
  • test initialize, tools/list, search planning, filtering, and handoff flows
  • review x402 integration boundaries
  • improve production documentation and deployment guidance
  • identify reliability, authentication, rate-limit, and monitoring gaps

Codex accelerated the project by moving from repository inspection to implementation, testing, and deployment verification within a single development workflow.

Architecture

  • GitHub Pages: human-facing demo and documentation
  • Remote MCP: free planning, filtering, and handoff tools
  • Paid API: automated production execution
  • x402: payment boundary for paid agent use
  • Base / USDC: settlement network and payment asset
  • Official MCP Registry: machine-readable discovery
  • GitHub: public source repository and setup documentation

Testing

Judges can test the project without rebuilding it:

  1. Open the public demo URL.
  2. Enter SaaS onboarding feedback and generate a noise-reduced search plan.
  3. Check the Remote MCP health endpoint.
  4. Connect an MCP-compatible client to the public Streamable HTTP endpoint.
  5. Call the available planning, filtering, and handoff tools.
  6. Use the synthetic feedback above to reproduce the filtering and handoff flow.
  7. Review the public repository README for setup and architecture details.

Planning, filtering, and handoff are free. Production execution and external data collection are separated behind the x402 paid API.

What is next

The next phase is to complete the full Feedback-to-PR loop: automatically preserve approved evidence, open a Codex engineering task, generate a patch, run verification, and return a traceable fix receipt.

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