hounddog MCP Server
hounddogai/hounddog
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Privacy Code Scanner and Dataflow Context Engine for AI coding agents
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hounddogai/hounddog is a Shell tool. Privacy Code Scanner and Dataflow Context Engine for AI coding agents Topics: privacy, sast, scanner, ccpa, dataflow-analysis, eu-ai-act, gdpr, iso-42001, pii-detection.
Ranked #4313 out of 24846 indexed tools.
Ecosystem
Shell No license
privacysastscannerccpadataflow-analysiseu-ai-actgdpriso-42001pii-detection
Score Breakdown
Stars 15% 129
129 stars → growing community
Freshness 25% 1mo ago
Last commit 1mo ago → moderate activity
Issue Health 25% 50%
1/2 issues closed → decent issue management
Contributors 10% 4
4 contributors → small team
Dependents 25% 0
No dependents → no downstream usage
npm Downloads N/A
PyPI Downloads N/A
Forks 61
Description Good
License None
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
How to Improve
Description low impact
License low impact
Freshness high impact
Matched Queries
From the README
# HoundDog.ai **Deterministic Privacy Code Scanner for GDPR Data Mapping Evidence and Proactive AI Governance** HoundDog.ai builds lightweight, Rust-based code scanners that give you a living, code-derived view of the services, APIs, data flows, and external integrations across the software you build. Deterministic analysis, refreshed on every commit, across every repo. > Need org-wide gRPC service mapping for your AI coding agents, covering every service, API, and field across monorepos > and microservices? See [ProtoMap](https://github.com/hounddogai/protomap). ## Privacy Code Scanner The [Privacy Code Scanner](https://hounddog.ai/privacy-code-scanning/) provides deterministic, code-level evidence of sensitive data flows across logs, storage, APIs, third parties, and AI integrations. Surveys go stale. Privacy platforms infer flows after deployment. AI-only approaches produce inconsistent evidence and waste tokens on discovery. Our engine continuously maps what developers actualRead full README on GitHub →
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