Package · PyPI
PyPI · v1.0.0 · MIT
Compact structural codebase MCP server for token-efficient AI development
Tentative categories were assigned with moderate confidence (0.5 to 0.75): they are shown, but they do not count toward a category’s momentum. How categories work
pip install token-saviorPyPI publishes no adoption figure, so there is no growth to derive. Nothing is estimated in its place.
Adoption history
PyPI publishes no adoption count, so there is no history to chart. That is a fact about the registry, not a gap in ours: the package’s releases and maintenance are measured instead.
In the Crest catalog
MCP server that gets Claude to 97.9% (188/192) on a real coding benchmark at -80% active tokens and -83% wall time, vs 78.3% plain. Structural code navigation + persistent memory engine. Works with every MCP client.
Linked by the package’s own registry metadata.GitHub (opens in a new tab)
>=6.0No published news event mentions this package or its repository yet.