Lightning Memory MCP Server

lightning-memory PyPI v0.7.0

Published by singularityjason — 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.

Decentralized agent memory for the Lightning economy. Nostr identity, L402 payments, semantic search.

Trust grade
A
96/100
Last scanned get badge →
Trust
A · 96/100
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
Moderate
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 = 96/100

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 − 4 = 96. 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
−3 capability blast radius (moderate) — client exposure if the model is manipulated
−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 22

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

  • memory_syncingests untrusted input
  • ln_agent_attestno sensitive capability
  • ln_agent_verifyno sensitive capability
  • ln_anomaly_checkno sensitive capability
  • ln_auth_lookupno sensitive capability
  • ln_auth_sessionno sensitive capability
  • ln_budget_checkno sensitive capability
  • ln_budget_setno sensitive capability
  • ln_budget_statusno sensitive capability
  • ln_compliance_reportno sensitive capability
Show 12 more tools ↓
  • ln_discover_gatewaysno sensitive capability
  • ln_preflightno sensitive capability
  • ln_remote_queryno sensitive capability
  • ln_spending_summaryno sensitive capability
  • ln_trust_attestno sensitive capability
  • ln_vendor_reputationno sensitive capability
  • ln_vendor_trustno sensitive capability
  • memory_editno sensitive capability
  • memory_exportno sensitive capability
  • memory_listno sensitive capability
  • memory_queryno sensitive capability
  • memory_storeno 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
v0.7.0 latest A 96/100 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 · 96/100
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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 lightning-memory --online --registry pypi

Use the free API → How scoring works

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