MCP Observatory
4m ago Tracked 59,451 Last 4m ago 24h 2,449
github Python analyzed d2de437

RalfHuesing/ComputeToAi

github

Generic stock-and-flow simulation engine via MCP server. First use case: retirement, liquidity, and financial planning.

maintainer
RalfHuesing
licence
MIT
first seen
2026-07-18
last seen
2026-07-26
releases · 30d
0
short id
risk 10/100 · heuristic grade
A minimal inferred analyzer v33 (current)
  • capability exposure inferred + 10

inferred

The A–E grade is our heuristic synthesis — a "review this" prompt, not a verdict. Each factor is tagged by what backs it: attested (a verifiable record), reported (a third party's claim), or inferred (our own heuristic, e.g. permissions). See methodology.

grade last moved 1w ago · see ecosystem CVEs →

risk trajectory 1 movements
  • A · 4 → A · 10
capability exposure grade factor +10
Inferred surface — each links to servers holding it:
vulnerabilities 0 CVEs

No known CVEs for this server.

tool safety all quiet

No tool-safety findings — heuristic detectors run on the compute-risk cadence; a finding appears when a tool trips a rule.

skills & danger signals github-tarball
prompt-surface shipped agent-instruction files + hidden-content / dangerous-code findings — quoted from the analyzed source

analyzed commit d2de437 · analyzer v33 · 3w ago

skills & prompt files 1

embed badge readme-ready
live risk-grade badge preview [![MCP Observatory risk grade](https://mcpobservatory.com/servers/github:RalfHuesing/ComputeToAi/badge.svg)](https://mcpobservatory.com/servers/github:RalfHuesing/ComputeToAi/security)

Heuristic, inferred signals — false positives (legitimately powerful tools, forks, language ports) are expected. Treat each as "review this", not a verdict. See the ecosystem-wide picture on the security hub, or the fleet security of RalfHuesing.