Give your AI coding assistant real code understanding — powered by the same engines VS Code uses
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"scope-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
[](https://pypi.org/project/scope-mcp/) [](https://pypi.org/project/scope-mcp/) # scope-mcp **A tool that gives your AI coding assistant superpowers.** Instead of guessing what your code does, it gets real answers straight from the same engines VS Code uses — so it actually understands functions, classes, types, and where things connect. No setup. No indexing. Just works. ## What's the problem? When you ask an AI to "find where `get_lsp` is called," most tools do a text search — like Ctrl+Shift+F. That returns *everything* named `get_lsp`: the definition, comments, imports, false positives. You have to dig through the noise. ``` ┌─ grep (text search) ─────────────────────┐ │ project.py:75 def get_lsp(self, lang) │ ← definition (not a call) │ project.py:110 return await get_lsp(lang)│ ← actual call │ project.py:117 lsp = await get_lsp(lang) │ ← actual call │ project.py:81 cfg = LSP_REGISTRY... │ ← noise │ project.py:109 async def get_lsp_for... │ ← noise │ 5 results · 3 are noise │ └───────────────────────────────────────────┘ ┌─ scope (smart search) ────────────────────┐ │ [call site] project.py:110 │ │ [call site] project.py:117 │ │ 2 results · 0 noise │ └───────────────────────────────────────────┘ ``` Scope asks the compiler instead. You get only what you actually asked for. [→ See the benchmarks](BENCHMARKS.md) — up to 25x fewer tokens, 0% false positives. ## What you can do with it | Instead of digging through files... | Just ask scope... | You get | |---|---|---| | Grepping for a function name and filtering out junk | `find_references("get_lsp")` | Only the places where it's actually called — no noise | | Reading a whole file to figure out what's in it | `explain_file("server.py")` | A clean summary: language, line co…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.