list_presets

list_presets

List all available analysis presets with their descriptions. Use this to discover which presets are available before calling analyze_architecture.

Tip

This tool is fast (no analysis required) and useful for exploring what metrics each preset includes.

Parameters

None. This tool takes no parameters.

Response

Returns a JSON array of preset objects.

Response Schema

[
  {
    "name": "string",
    "description": "string"
  }
]

Example

Request:

{}

Response:

[
  {
    "name": "quick",
    "description": "SCC, L1 overview, centrality"
  },
  {
    "name": "ci",
    "description": "SCC, smells, layer violations, architecture budgeting"
  },
  {
    "name": "risk",
    "description": "Ricci curvature, MSR, hotspot score, ownership, truck factor, causal discovery, semantic duplicates"
  },
  {
    "name": "coupling",
    "description": "Propagation cost, msr (hotspot + co-change), coupling analysis, decoupling level, clustered cost cochange, semantic duplicates"
  },
  {
    "name": "security",
    "description": "Security, sensitive data flow, effect violations"
  },
  {
    "name": "runtime",
    "description": "Cloud cost, traffic hotspot, critical path, runtime centrality, runtime drift, sensitive data flow, test coverage"
  },
  {
    "name": "ai",
    "description": "LLM integration, ML architecture, RAG architecture, finetuning architecture, agent architecture"
  },
  {
    "name": "rag",
    "description": "RAG architecture, LLM integration"
  },
  {
    "name": "full",
    "description": "All metrics"
  }
]

Preset Details

quick

Speed: Fast (~1-5 seconds)

Metrics: scc, l1_overview, centrality

Use case: Pre-commit sanity check, quick health assessment

Best for: Daily development workflow, pre-PR checks

---

ci

Speed: Moderate (~5-15 seconds)

Metrics: scc, smells, layer_violations, architecture_budgeting

Use case: CI/CD pipeline gate, policy enforcement

Best for: Pull request checks, merge gates, architecture governance

---

risk

Speed: Moderate (~10-30 seconds)

Metrics: ricci_curvature, centrality, msr (includes hotspot score), ownership, truck_factor, causal_discovery, semantic_duplicates

Use case: Defect prediction, change risk assessment, knowledge risk

Best for: Release planning, identifying risky modules, bus factor analysis

---

coupling

Speed: Slow (~30-90 seconds)

Metrics: propagation_cost, msr (includes hotspot and co-change analysis), coupling_analysis, decoupling_level, clustered_cost_cochange, semantic_duplicates

Use case: Deep coupling audit, refactoring planning

Best for: Major refactoring initiatives, decoupling analysis, architectural reviews

---

security

Speed: Moderate (~10-20 seconds)

Metrics: security, sensitive_data_flow, effect_violations

Use case: Security audit, CVE detection, PII flow analysis

Best for: Security reviews, compliance checks, vulnerability assessment

---

runtime

Speed: Moderate (~15-30 seconds)

Metrics: cloud_cost, traffic_hotspot, critical_path, centrality, runtime_drift, sensitive_data_flow, test_coverage

Use case: Runtime/performance analysis (requires telemetry data)

Best for: Performance optimization, cloud cost analysis, observability review

Note

The runtime preset requires telemetry data (traces, logs, metrics). Without runtime data, some metrics will return empty or default values.

---

ai

Speed: Moderate (~10-25 seconds)

Metrics: llm_integration, ml_architecture, rag_architecture, finetuning_architecture, agent_architecture

Use case: LLM/AI integration health check, ML pipeline audit

Best for: AI-powered applications, LLM integration reviews, RAG system analysis

---

rag

Speed: Fast (~5-10 seconds)

Metrics: rag_architecture, llm_integration

Use case: RAG-specific analysis (retrieval, grounding, index staleness)

Best for: RAG applications, vector database integration, retrieval quality

---

full

Speed: Very slow (~1-5 minutes for large projects)

Metrics: All 52 metrics in the engine (14 OSS + 38 Pro)

Use case: Comprehensive architecture review

Best for: Quarterly reviews, architecture assessments, research

Caution

The full preset can take several minutes on large projects. Use more focused presets for routine checks.

Choosing a Preset

Scenario	Recommended Preset
Pre-commit check	quick
Pull request gate	ci
Planning a refactor	coupling
Security review	security
AI system audit	ai or rag
Performance review	runtime
Quarterly architecture review	full
Identifying risky modules	risk

Workflow

Use this tool to explore available presets before analysis:

1. list_presets → see all options
2. Pick appropriate preset based on use case
3. analyze_architecture({ preset: "..." }) → run analysis

Error Cases

This tool has no error cases — it always succeeds and returns the preset list.

Performance

• Speed: Instant (<10ms)
• Caching: Not applicable (no computation)

Related Tools

• analyze_architecture — Run analysis with a preset

Related Guides

• Presets Guide — Detailed guide on all presets
• Choosing the Right Preset — Decision guide

CLI Equivalent

# List presets using CLI
arxo analyze --help | grep -A 20 "presets"

# Or list via config
arxo config presets