Hi all, I’m Vincenzo. Since my tip 3 days ago got decent reception, I figured I
owed the community the bigger picture — what this actually is, why we built it,
and what’s emerging in the real usage data.
The problem, in three pieces
Millions of AI coding agents — ChatGPT, Claude, Cursor, Copilot, Windsurf —
recommend npm / PyPI / Cargo packages every day to millions of developers. Three
things are broken at the same time:
-
Tokens burned at scale. Each agent fetches raw registry JSON — ~3 KB of keys
the model doesn’t actually need — to make a single install decision. Repeated
billions of times a day across every agent, it’s real LLM input cost and real
energy on the compute side. -
Stale training data. Cut off 6-12 months before the answer: recent CVEs
missed, deprecated libraries still suggested, package names occasionally
hallucinated (I’m seeing API requests for packages that don’t exist — the model
invented the name based on a plausible pattern). -
No shared layer. Every agent hits the public registries independently,
reinventing the lookup. Zero coordination, zero cache efficiency, zero
consolidated signal.
What DepScope is
depscope.dev is the shared layer: OSV + GitHub Advisory Database as a live API,
a dedicated endpoint that returns a 74%-smaller payload than raw registry JSON
for the same install decision, and an MCP server for direct Claude / Cursor
integration. Covers 17 ecosystems (npm, PyPI, Cargo, Go, Maven, NuGet, RubyGems,
Composer, Pub, Hex, Swift, CocoaPods, CPAN, Hackage, CRAN, Conda, Homebrew).
402 vulnerabilities tracked with cross-ecosystem correlation. Open
infrastructure, MIT client SDK, EU-hosted.
Who we are, briefly
DepScope is a diversification project inside a company that has been operating
in digital for years. We haven’t raised VC and we’re not looking to. This gives
us a very different cost structure from the usual supply-chain security players
— and the freedom to keep the public API genuinely public, without quarterly
conversion pressure. When traffic scales beyond a certain threshold there will
be real infrastructure costs; we’ll cover them via parallel revenue streams
already in design (API Pro for commercial SLAs, threat-intel data feeds, private
instances) — none of which involves monetizing the public API an AI agent
queries. The neutrality of a verification layer depends on its independence.
The underlying positioning, for context: OSV (Google) and Sigstore (Linux
Foundation) are precedents of neutral public infrastructure for the supply chain
— but they were designed for humans and CI pipelines, returning raw JSON.
DepScope tries to do the same job from an AI-native perspective: useful
semantics in one round-trip, lightweight payload, endpoints shaped for LLM
decisions rather than human reading. I don’t know of anyone else building that
specific layer today.
What’s emerged in 72h of public data
- 334 npm downloads of the MCP server (organic, zero ads)
- 13,800+ API calls on /api/check/* today alone
- The hallucinated-package signal is real — I see it daily in the logs.
Model-invented names that follow naming conventions but don’t exist in any
registry - Most-queried by agents so far: express, axios, fastapi, drizzle-orm, requests,
lodash, zod
How to use it
ChatGPT Custom Instructions:
Before suggesting any npm/pip/cargo install, call:
GET https://depscope.dev/api/check/{ecosystem}/{package}
If deprecated=true or risk_level=critical, propose the alternatives
field instead. Cite the health score in your answer.
Custom GPT: ChatGPT - DepScope
Claude Code / Cursor / Windsurf / Claude Desktop (MCP server, one-liner):
claude mcp add depscope – npx -y depscope-mcp
Full integration examples: DepScope — Package Intelligence for AI Agents
What I’d ask the community
If you’ve hit cases of ChatGPT suggesting:
- a deprecated package (“Yes, use request for HTTP…”)
- a hallucinated package that doesn’t exist on npm/PyPI
- a vulnerable library without mentioning the known CVE
Drop the example in the thread. I’m collecting real cases for a follow-up post
on patterns — specifically which naming conventions trigger hallucination most
often. It’s the most interesting signal emerging right now and I can only see it
partially from server-side.
Happy to answer technical questions on the API/MCP design or the data pipeline —
but for anything longer-form, email works better for me than threads.
Vincenzo