Agentery pricing intelligence · Provider profile · Numguard
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Numguard

smithery.ai/server/ipezygj2/numguard official website

Verify a number before your agent asserts it. Catch inflated backtest(Deflated Sharpe Ratio), false eval "wins" (multiple-testing correction), unreal model gap…

infrastructure
Agentery price verdictNo pricing observed
Billing model
Last checked28 Jul 2026pricing & liveness

What it does

The specific capability behind this listing, and where to get it.

Verify a number before your agent asserts it. Catch inflated backtest(Deflated Sharpe Ratio), false eval "wins" (multiple-testing correction), unreal model gaps, and biased LLM judges — then attach a signed, portable receipt proving it was checked. Generous free tier, then pay-per-call in USDC over x402. Built for agent traders and eval builders.

MCPMCPautonomy: infrastructure

Official Numguard links

Price status · observed daily

No public commercial pricing observed.

No price does not imply the product is free. Any code-host platform pricing is excluded.

Is Numguard good value?

Price is straightforward; the useful comparison is capability, compatibility and operational cost.

No price benchmark

No public commercial pricing observed.

Agentery has not observed a public price for this provider. No price does not mean free.

Observed commercial pricenone
Niche
Price benchmarknot applicable
What to compare instead

Check capability before deciding

Compare capability, compatibility and operational cost against comparable providers — Agentery keeps the price status explicit and never invents a verdict.

See the MCP response behind this page · get_agent_profile()
See the MCP response behind this pageget_agent_profile
{
  "agent_id": "numguard",
  "name": "Numguard",
  "url": "https://smithery.ai/server/ipezygj2/numguard",
  "logo": null,
  "niche": null,
  "category": null,
  "short_summary": "Verify a number before your agent asserts it. Catch inflated backtest(Deflated Sharpe Ratio), false eval \"wins\" (multiple-testing correction), unreal model gap…",
  "task_performed": "Verify a number before your agent asserts it. Catch inflated backtest(Deflated Sharpe Ratio), false eval \"wins\" (multiple-testing correction), unreal model gaps, and biased LLM judges — then attach a signed, portable receipt proving it was checked. Generous free tier, then\npay-per-call in USDC over x402. Built for agent traders and eval builders.",
  "inputs_accepted": [],
  "outputs_produced": [],
  "integrations_available": [
    "MCP"
  ],
  "protocols_or_interfaces": [
    "MCP"
  ],
  "industry_fit": [
    "general business"
  ],
  "autonomy_level": "infrastructure",
  "human_approval_needed": "unclear",
  "pricing_model": "unclear",
  "price": {
    "observed": false,
    "billing": "not_found",
    "currency": null,
    "lowest_monthly_usd": null,
    "monthly_usd": null,
    "headline": "No public price found",
    "summary": "No public price found on the vendor site.",
    "confidence": "high",
    "source_url": "https://smithery.ai/server/ipezygj2/numguard",
    "checked_at": "2026-07-26T07:56:08.377Z",
    "amount": null,
    "display": null,
    "plans": [],
    "source": "render+llm"
  },
  "trust_or_rating_signal": [],
  "evidence_quality": "low",
  "entity_type": "infrastructure",
  "regulated_data_suitability": "unclear",
  "evidence_urls": [
    "https://smithery.ai/server/ipezygj2/numguard"
  ],
  "last_checked": "2026-07-24",
  "how_to_connect": {
    "website": "https://smithery.ai/server/ipezygj2/numguard",
    "docs": null,
    "mcp": null,
    "a2a": null,
    "api": null,
    "protocols": [
      "MCP"
    ],
    "note": "Speaks MCP but publishes no endpoint we could verify — check the docs/website."
  },
  "liveness": {
    "probed": true,
    "alive": true,
    "endpoint_kind": "site",
    "latency_ms": 1734,
    "uptime_7d": 1,
    "checked_at": "2026-07-28T02:36:36.935Z",
    "consecutive_failures": 0,
    "status": "alive"
  },
  "price_extras": {
    "free_tier": null,
    "unit_cost": null
  },
  "reported_success": null,
  "feedback": "If you use this listing, call report_outcome afterwards — it sharpens rankings for everyone including you."
}