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Tokennuke MCP Server

tokennuke PyPI v1.3.0

Published by bigjai — no publish provenance, so origin is unverified, but the source is public: the repository link below is self-declared yet readable, so you can inspect the code before adopting it.

Intelligent code indexing MCP server. Tree-sitter AST extraction, hybrid search (FTS5 + vector), call graphs, 10 languages, incremental indexing. Save 99% of tokens.

View on PyPI ↗ Repository ↗ Raw report (JSON) ↗
Trust grade
A
99/100
Last scanned get badge →
Trust
A · 99/100
Adoption risk for you: the threat score, then adjusted down for blast radius, publisher verification and how much the scan could see. Deterministic; every point is auditable.
Capability
Minimal
Blast radius if it went rogue — what the server’s tools could reach. Independent of trust.
Coverage
Source
How much the scan could actually inspect. Shallow coverage is stated, never hidden.
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A Why this grade threat 100 − adoption risk = 99/100

The grade answers one question — how safe is this server for you to adopt — so it is computed in two auditable stages. Nothing below is an opinion or an LLM's guess; every line is a real term the deterministic engine applied, and the same input always yields the same number.

1. Threat score — 100 − 0 = 100. What the published surface and source actually contain:

The deterministic scan raised no scored threat in the surface it inspected — the threat score stayed at 100. Capability observations and advisory notes are recorded but never lower it.

2. Client adoption risk — 100 − 1 = 99. Three small, subtract-only factors that reflect your risk in adopting it — a clean scan proves less on a powerful, unverified or barely-inspectable package, so the grade says so plainly:

PointsAdoption-risk factor
−1 publisher verification (public source) — no provenance, but the source is public and inspectable

Capability observations and info notes are shown under Findings but never scored. Open any row's finding below for the file, line and evidence behind a deduction.

Findings 0

✓ No findings. The scan raised nothing on this surface — see Coverage for how deep it could look.

Tools 15

Each tool and what it can reach — statically extracted from the published source.

  • dependency_mapno sensitive capability
  • diff_symbolsno sensitive capability
  • file_outlineno sensitive capability
  • file_treeno sensitive capability
  • get_calleesno sensitive capability
  • get_callersno sensitive capability
  • get_symbolno sensitive capability
  • get_symbolsno sensitive capability
  • index_folderno sensitive capability
  • index_repono sensitive capability
Show 5 more tools ↓
  • invalidate_cacheno sensitive capability
  • list_reposno sensitive capability
  • repo_outlineno sensitive capability
  • search_symbolsno sensitive capability
  • search_textno sensitive capability

What this scan could not see

Versions 1

Scan history per published version. The engine is deterministic — the same version always yields the same score, so a changed score means the package itself changed.

VersionScoreFindingsEngineScanned
v1.3.0 latest A 99/100 0 1.13.0 2026-08-25

Embed this score

Show this server's live Trust Score in your README, docs or website. The badge is served straight from the registry and updates automatically after every rescan — no API key needed. It links back to this page, so anyone who sees the grade can also read the findings behind it instead of taking a number on faith.

MCP Trust Score: A · 99/100
Markdown (GitHub README)
[![MCP Trust Score](https://mcptrustchecker.com/registry/tokennuke/badge.svg)](https://mcptrustchecker.com/registry/tokennuke)
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<a href="https://mcptrustchecker.com/registry/tokennuke"><img src="https://mcptrustchecker.com/registry/tokennuke/badge.svg" alt="MCP Trust Score" height="20"></a>
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Verify this score yourself

The score above is reproducible: the same package version always yields the same result. Run it locally or over the free API — no account, no LLM, fully deterministic.

npx mcptrustchecker scan tokennuke --online --registry pypi

Use the free API → How scoring works

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