MCP.Market

Aiglare MCP server

by nugehs·io.github.nugehs/aiglare·v0.2.3·1 stars

Audit AI/LLM features for governance guardrails: confidence, fallback, validation, human-in-loop.

B82/100grade B
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Safety scan
B82/100

full report

Adoption
New

1 stars47 downloads/wk

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If you have run it, two minutes of your experience saves the next person an afternoon.

Aiglare tools (3, 1 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • ai_surface_audit

    Audit 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 action

    Run 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_providers

    List 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

no findings
  • 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
Overall 82/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

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
Add to Cursor

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 CI

What 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 lint

Supported 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.html

Provider-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 Chat
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  • Roast My Design System
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  • CHAP Coordinator
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    A
  • Roast My Design System
    Audit your Design System and serve your Agent the rules that keep AI-written UI on-system.
    A
  • three.ws Provenance
    Append-only, signed, on-chain-verifiable agent action log — record and audit what agents did.
    B

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