Aiglare MCP server
Audit AI/LLM features for governance guardrails: confidence, fallback, validation, human-in-loop.
1 stars47 downloads/wk
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Aiglare tools (3, 1 write)
write = sends, deletes, buys or postsRead from the package source without running it. The installed server may list more.
ai_surface_auditAudit a JS/TS repo for AI/LLM governance guardrails. Returns every surface where model output reaches a user or a side-effect, classified by sink and scored on confidence, fallback, validation, human-in-the-loop, and error isolation. Uses a repoctx index automatically if present.
ai_surface_gatewrite actionRun the CI gate for a repo: reports whether any red + side-effectful surface exists (the "AI auto-triggers an irreversible action with no confirmation" case). passed=false means a build should fail.
list_providersList the AI/LLM provider registry the scanner detects (package names and inference hosts).
Public scan report
scanner v0.1.9 · 2026-09-27 · same rubric, same numbers if you re-run it
- Code scan8 source files scanned25/25
- –Live reliabilityno gateway calls yet and no remote to proben/a
- –Tool poisoningtools not inspected (local package is not executed); not countedn/a
- Auth qualitylocal package, no credentials required12/15
- Maintenancelast push 105 days ago8/15
- Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
How Aiglare compares
Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.
- Safety score
- 82/100
- higher than 65% of 4,460 AI and agents servers
- Tools
- 3
- the typical AI and agents server has 6
- Tools that change things
- 33%
- typical: 0%. Fewer write tools means less an agent can break
- GitHub stars
- 1
- more than 50% of 2,711 AI and agents servers
- Downloads a week
- 47
- more than 39% of 1,538 AI and agents servers with a package
What its tools let an agent do: 2 read · 1 change
Install Aiglare in Claude Code, Cursor or VS Code
Runs npx -y @nugehs/aiglare on your machine. Read the scan report first; the gateway never runs local packages.
claude mcp add aiglare -- npx -y @nugehs/aiglare
What the publisher says
From the Aiglare repository's README, as published. We do not edit it. Read it on GitHub
aiglare
Lint your AI features for governance guardrails — where can the model do something you can't undo?
Live site: nugehs.github.io/aiglare-web
Point it at any JS/TS repo and it finds every place an LLM/AI output reaches a user or triggers a side-effect (payment, booking, email, database write) — then flags which of those have no confidence handling, no fallback, no output validation, and no human-in-the-loop.
Most AI incidents aren't model failures. They're governance failures: the model output flowed straight to a user or an irreversible action with nothing in between. This tool makes those paths visible, and lets you block them in CI.
npx @nugehs/aiglare # audit current repo
npx @nugehs/aiglare ./src --ci # fail the build on a red side-effectful surface
npx @nugehs/aiglare --compliance all --format html # HTML evidence report for auditors
npx @nugehs/aiglare --compliance soc2,eu-ai-act --format lint --ci # compliance linter in CIWhat it reports
Each AI surface is classified by sink — where the output goes:
- user-facing — returned from a route/controller, or rendered in a component
- side-effectful — feeds a payment, booking, email, db/file write, or shell
- internal — logged or cached only
…and scored on five guardrail dimensions: confidence handling, fallback/uncertain path, output validation, human-in-the-loop (for side-effects), and error isolation.
The CI gate (--ci) fails only on red + side-effectful surfaces — the "AI auto-triggers an irreversible action with no confirmation" case — so it's safe to adopt without drowning a team in warnings.
Compliance reports
Pass --compliance to map every finding to specific regulatory controls, then pick your output format:
# HTML evidence report — branded, collapsible per-framework sections, guardrail matrix
aiglare --compliance all --format html > report.html
# ESLint-style linter — pipe into CI, editors, or reviewers
aiglare --compliance soc2,eu-ai-act --format lint
# Markdown evidence doc — ready to hand to an auditor
aiglare --compliance all --format markdown > AUDIT.md
# Guardrail linter (no framework) — rule IDs like guardrail/human-in-loop
aiglare --format lintSupported frameworks:
Use all to include every framework. Each surface in --json output gains a violations[] array with the framework, control ID, description, and error/warning level.
Multi-repo: pass multiple paths for a single combined report:
aiglare ./api ./web --compliance all --format html > report.htmlProvider-agnostic
Shortened. The full README is on GitHub.
Nothing above is checked by us. What we check is on the safety report.
Aiglare: common questions
- Is Aiglare MCP server safe?
- Mostly: it is graded B (82/100). Read the Aiglare safety report
- How do I install Aiglare?
- It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
- Does Aiglare need an API key?
- Not as far as the registry entry and our scan can tell: no credentials are declared or required.
- Is Aiglare maintained?
- The last commit was 144 days ago (2026-06-13). The latest release is v0.2.3.
- What can I use instead of Aiglare?
- Servers from other publishers that do the same job: Agent-Native Chat MCP server, Roast My Design System MCP server and CHAP Coordinator MCP server.
- How does Aiglare compare with other AI and agents MCP servers?
- safety score 82/100 (higher than 65% of 4,460 AI and agents servers); tools 3 (the typical AI and agents server has 6); tools that change things 33% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.
Alternatives to Aiglare
Same job from other publishers: the closest match first, then the best rated.
- Agent-Native ChatMinimal chat-first app with durable threads, actions, and the app-agent loopnot reviewedEstablishedA
- Roast My Design SystemAudit your Design System and serve your Agent the rules that keep AI-written UI on-system.not reviewedEstablishedA
- CHAP CoordinatorAuditable records of human decisions over AI agent work. Approvals, edits, overrides, escalations.not reviewedEstablishedA
- Roast My Design SystemAudit your Design System and serve your Agent the rules that keep AI-written UI on-system.not reviewedEstablishedA
- three.ws ProvenanceAppend-only, signed, on-chain-verifiable agent action log — record and audit what agents did.not reviewedEstablishedB