Public whitepaper · v1.0
Building & Commercializing a Verified MCP Ecosystem Database
A feasibility and business plan for turning MCPera from a static MCP directory into a verified, traceable, licensable supply-chain database for Model Context Protocol servers.
§Executive summary
In 2026, MCP (Model Context Protocol) is the de-facto standard for connecting AI agents to tools, and server supply has exploded: the official Registry lists 1,600+ unique servers after dedup within our sampled range, the Smithery public API reports 19,932, the PulseMCP sitemap contains 21,710 server pages, and npm lists 9,775 packages under the mcp-server keyword. Yet every incumbent competes on coverage.
The open, defensible gap is verification: whether a server actually works today, on which clients it runs, whether and how it charges, how auth is configured, and when it was last down. This plan proposes a narrow-but-true Verified MCP Supply-Chain Database built on compliant public sources plus a proprietary verification layer, distributed through programmatic SEO/GEO and monetized via B2B data licensing, premium subscriptions, and referrals.
Why now
Supply is exploding while trust is scarce; Glama's commercial Data License (key required, no unauthorized redistribution) is direct evidence that structured MCP data already commands payment.
Why us
MCPera already owns the category name and a vertical MCP position; the missing piece is a real data asset, which this plan builds without leaving the zero-cost static model.
Moat
Not raw listings (a commodity) but the verification layer, field-level provenance, client-compatibility matrix, and time-series history — costly to produce and defensible to license.
Approach
Immutable raw snapshots → normalized records → verification gate → static HTML/JSON projections. Pluggable adapters and config-driven facets keep it extensible.
§Contents
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01Positioning & One-Page Conclusion
The one-page conclusion: a real, measured opportunity, the verification gap nobody owns, and a focused recommended play.
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02Competitive Analysis & Target Users
Who already plays (Registry, Smithery, PulseMCP, Glama), measured scale, their weaknesses, and who actually pays.
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03Technical Feasibility: Collection & Verification
Where the data comes from, how crawlers and AI-assisted extraction work, and how to keep every field true over time.
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04Product, Programmatic SEO, Business Model & Risks
Turning records into traffic and leads, pricing the data, the 90-day roadmap, and the legal/technical/commercial risks.
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0AAppendix A — Programmatic SEO & GEO Architecture
The engineering blueprint for the future database: data model, pipeline, URL schema, structured data, GEO feeds and extensibility.
Scope, sources & method
Every scale figure was measured on 2026-10-13 against public APIs, sitemaps, and GitHub; each item cites its source and verification date, and anything not measured is explicitly marked unverified. Data collection uses only publicly accessible information, respects terms of service and robots.txt, and never mirrors licensed third-party datasets — it links to and cites them. This is a planning document describing an intended product direction, not a guarantee of availability.
Continue to the positioning & one-page conclusion →