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io.github.uchit/aipatterns-mcp-server

NPM · AIPATTERNS-MCP-SERVER · SCANNED AUG 3

Search AU enterprise AI patterns, benchmarks, incidents, and regulatory changes.

+23 this week 69 Trust /100
Trust breakdown (6 categories)

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score →

Supply Chain Security86
  • No malware found by supply-chain analysis.Pass
  • Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree.Partial
  • No install/post-install scripts declared.Pass
  • Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree. View diagnostics → Partial
Provenance & Transparency45
Schema Quality & AI Usability77
  • AI-judged instruction clarity (excellent).Pass
  • Tool/resource definitions use about 465 tokens (~93/item across 5 items; 5 tools + 0 resources), lean.Pass
  • Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management23
  • Stability observed for 7 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
  • 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
  • 100% of tool parameters carry a description.Pass
Capabilities100
  • Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Install

Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.

npm · aipatterns-mcp-server

# add to Claude Code
claude mcp add uchit-aipatterns-mcp-server -- npx -y aipatterns-mcp-server
# add to Codex CLI
codex mcp add uchit-aipatterns-mcp-server -- npx -y aipatterns-mcp-server
// opencode.json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "uchit-aipatterns-mcp-server": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "aipatterns-mcp-server"
      ],
      "enabled": true
    }
  }
}
# add to OpenClaw
openclaw mcp add uchit-aipatterns-mcp-server --command npx --arg -y --arg aipatterns-mcp-server
# ~/.hermes/config.yaml
mcp_servers:
  uchit-aipatterns-mcp-server:
    command: "npx"
    args: ["-y", "aipatterns-mcp-server"]
// mcp.json
{
  "mcpServers": {
    "uchit-aipatterns-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "aipatterns-mcp-server"
      ]
    }
  }
}
Changelog

Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.

  • 3 Aug 26 +4
    • Stability: unverified → 0.23 functional
  • 2 Aug 26 +44
    • Provenance: unverified → fail security
    • Known CVEs: unverified → partial security
    • Install scripts: unverified → pass security
    • Malware scan: unverified → pass security
    • Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window). security
    • Security disclosure: fail → unverified functional
    • Tool coverage: 100 → unverified functional
    • Schema quality: unverified → excellent functional
    • License: unverified → pass functional
    • Dependency health: unverified → partial functional
    • Maintenance: unverified → pass functional
    • MCP protocol: unverified → pass functional
    • First check of Schema quality: unverified functional
    • Licence: MIT functional
  • 31 Jul 26 −7
    • We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
  • 30 Jul 26 −18
    • Malware scan: pass → unverified security
  • 27 Jul 26 46

    First indexed and scored.

Diagnostics

Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.

Captured 3 Aug 2026 · Analysed npm/[email protected]

Provenance none

Ecosystem: npm · Outcome: none

Dependencies 94 packages

94 packages in the resolved dependency tree · 94 deprecated · 29 stale.

The dependency tree was only partially resolved, so these counts may be incomplete.

MCP tools — 5 exposed · ~465 tokens

The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability.

Tool Tokens
get_incidents ~92

Retrieve notable Australian AI incidents. Useful for understanding real-world failures, regulatory enforcement actions, and which patterns could have prevented the incident.

NameTypeReqDescription
limitnumberMaximum number of incidents to return (default 5)
sectorstringFilter by sector: banking, insurance, government, retail, healthcare, utilities
severitystringFilter by severity: critical, high, medium, low

No output schema declared.

No examples provided.

get_pattern ~62

Retrieve full detail of a specific AI pattern from aipatterns.com.au, including implementation guidance and regulatory context. Use the slug returned by search_patterns.

NameTypeReqDescription
slugstringyesPattern slug, e.g. "agentic-ai/agent-checkpoint-and-recovery"

No output schema declared.

No examples provided.

get_regulatory_changes ~107

Retrieve recent and upcoming Australian AI regulatory changes (APRA, OAIC, ASIC, TGA, Privacy Act reform). Useful for understanding compliance obligations when building AI systems for the Australian market.

NameTypeReqDescription
impact_levelstringFilter by impact level: critical, high, medium, low
limitnumberMaximum number of changes to return (default 5)
regulatorstringFilter by regulator abbreviation: APRA, OAIC, ASIC, TGA

No output schema declared.

No examples provided.

get_sector_benchmark ~79

Get the AU AI Maturity Index benchmark score for a specific sector. Returns overall score, dimension scores (adoption, governance, investment, incidents), sector rank, and national averages. Scores computed from Q2 2026 evidence base.

NameTypeReqDescription
sectorstringyesOne of: banking, insurance, government, retail, healthcare, utilities

No output schema declared.

No examples provided.

search_patterns ~125

Search the aipatterns.com.au AI pattern library. Returns matching patterns with slug, title, description, maturity level, and category. Useful for finding design patterns relevant to a specific AI use case, capability, or compliance concern.

NameTypeReqDescription
categorystringOptional category filter (e.g. agentic-ai, governance, security, rag, observability, compliance, human-in-the-loop)
maturitystringOptional maturity filter (e.g. production, beta, experimental)
querystringyesSearch term to match against pattern title, description, or content

No output schema declared.

No examples provided.