dns-aid-core: DNS-based Agent Identification and Discovery - Reference Implementation for IETF BANDAID

by dns-aid · MCP Server · ★ 74

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How to install dns-aid-core

pip install "dns-aid[cli,mcp] @ git+https://github.com/dns-aid/dns-aid-core.git"
git clone https://github.com/dns-aid/dns-aid-core.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 68/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

a2aagent-discoveryai-agentsbandaiddnsdns-aiddnssecietflinux-foundationmcp

Quick Facts

Stars74
Forks32
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score68/100
Open Issues35
Last Updated2026-10-30
Created2026-02-05
Platformsmcp, python
Est. Tokens~25k

About dns-aid-core

DNS-AID DNS-based Agent Identification and Discovery Reference implementation for IETF draft-mozleywilliams-dnsop-dnsaid-02. DNS-AID enables AI agents to discover each other via DNS, using the internet's existing naming infrastructure instead of centralized registries or hardcoded URLs. Relationship to IETF The DNS-A

Compatible Skills

These tools work well together with dns-aid-core for enhanced workflows:

  • jules — semantic(0.27)+complementary+same_lang+similar_pop+shared_platform (55%)
  • a2a-adapter — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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