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3 posts tagged with "Methodology"

Evaluation methods, scoring rubrics, and research process

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HUD FMR and Income Limits with AI: Housing Research Needs Source Data

· 4 min read
MCPBundles

Housing research questions are easy to ask and easy to answer badly.

"Is this county affordable?" "What does HUD say about rent here?" "Which income limit should I use?" "How much cost burden shows up in CHAS?"

A language model alone will blur Fair Market Rent, income limits, MTSP tables, and CHAS affordability data into one vague paragraph. The HUD Housing Data MCP server pulls the official HUD rows first, then explains what they mean — with geography and year range spelled out.

AI housing research dashboard showing HUD Fair Market Rent, income limits, and CHAS affordability cards

UK House Price Data with AI: EPC, Land Registry & Price-Per-Square-Foot Evidence

· 5 min read
MCPBundles

TL;DR

  • Query 30.8 million EPC certificates, 2.7 million UK postcodes, and 1.34 million persisted Land Registry–EPC matches through the UK Property Intelligence app and MCP server.
  • Resolve an address or postcode, pull HM Land Registry sold prices, join EPC floor area where the match is strong, and return price-per-square-foot bands with explicit confidence flags—not a black-box valuation.
  • Built for lenders, property analysts, retrofit planners, and AI agents who need show your working evidence before formal RICS sign-off.

Most AI valuation demos make the same mistake. A user types an address, the model returns a number, and everyone pretends the answer came from evidence.

That is backwards. UK property questions are only useful when the agent can show its working: nearby sold prices, EPC floor area, property type, transfer dates, postcode geography, match confidence, and the gaps where public data is thin.

We built UK Property Intelligence around that evidence loop — a bounded, inspectable report from sold prices, EPC records, and postcode context, not a false-certainty number.

UK property valuation evidence dashboard with sold-price cards, EPC rating tiles, postcode map, and an AI agent confidence indicator

How We Score MCP Server Security: 18 Rules, Two Published Taxonomies, Zero Invented Checks

· 8 min read
MCPBundles

You paste an MCP server URL into a security analyzer. It spits out a number. You ask the obvious question: what does that number actually mean?

Most MCP scanners can't answer it. They run a bunch of regex, run a bunch of LLM prompts, and produce a verdict. If you push on the verdict, you find ad-hoc heuristics with no published source — and worse, you find marketing claims about "AI-powered security analysis" that nobody can audit.

We built MCPBundles' analyzer the other way around. Every rule cites a published taxonomy entry. If we can't cite an entry, the rule doesn't ship. The catalog is small, deliberate, and live: www.mcpbundles.com/learn/mcp-security.

This post is the "show your work" version of that page.