Humanity Score Checker

Evidence-backed human-impact rating for AI products.

Humanity Score Checker asks a simple question: does an AI product leave people more capable, fairly served, and meaningfully connected?

Version 2 separates two very different things:

• Self-assessment — a founder/user can enter three 0–100 ratings. This is useful for reflection, but it is always labeled SELF-ASSESSED and is not badge eligible.

• Evidence-backed audit — structured findings with source URLs are scored against a published rubric. Only audits that meet evidence coverage gates can receive an EVIDENCE-BACKED badge.

MCPMarket launch price: $19
Repository: github.com/oluwafemidiakhoa/humanity-score-checker

Humanity Score is a product-impact rating. It is not a regulatory, legal, safety, compliance, or third-party certification.

Why v2 exists

The original prototype averaged three user-supplied numbers. That made it fast, but not independently meaningful. v2 keeps that interface for compatibility while moving the product toward a reproducible, source-gated audit.

There are no hard-coded TAM claims, no claim that data was scraped live, and no claim that a self-entered score is verified.

Related MCP server: Designesy

Rubric

Each of 12 criteria starts at a neutral score of 50. Evidence shifts only the criterion it supports.

Agency

• user_control

• reversibility

• transparency

• human_override

Value Distribution

• user_benefit

• data_rights

• lock_in

• incentive_alignment

Human Connection

• collaboration

• substitution_risk

• social_wellbeing

• accessibility

Evidence uses:

• impact: -2 to +2

• confidence: 0 to 1

• source_type: primary, secondary, or anecdotal

Source type weights are 1.0, 0.75, and 0.50 respectively. Scores are clamped to 0–100.

Badge thresholds

• GREEN: 70–100

• YELLOW: 40–69

• RED: 0–39

A color does not automatically mean the badge is evidence-backed.

To become badge eligible, an audit must contain at least:

• 6 accepted findings

• 3 unique source URLs

• evidence across all 3 dimensions

• 6 distinct rubric criteria

• 2 primary-source findings

Until those gates pass, the badge is marked PROVISIONAL.

Recommended tool: audit_product

{
  "product_name": "Example AI",
  "product_url": "https://example.com",
  "description": "AI assistant for collaborative research",
  "evidence": [
    {
      "dimension": "agency",
      "criterion": "user_control",
      "finding": "Users can disable automated actions and choose manual control at any time.",
      "source": "https://example.com/docs/control",
      "source_type": "primary",
      "impact": 2,
      "confidence": 1.0
    }
  ],
  "human_story": "Optional reported user context; it is not silently treated as verified evidence."
}

The result includes:

• Humanity Score 0–100

• criterion and dimension scores

• GREEN / YELLOW / RED band

• evidence coverage and confidence

• rejected-evidence reasons

• deterministic SHA-256 report hash

• evidence-backed or provisional badge

• factual five-post share thread

• limitations

Backward-compatible tool: score_product

The original interface still works:

product_name, description, agency, value_capture, connection, sources, human_story

But the output is explicitly:

assessment_mode = self_reported
badge_eligible = false

This prevents a founder from entering 100, 100, 100 and presenting the result as an independently supported badge.

Other tools

generate_badge

Generates an UNVERIFIED visual badge for a standalone score. Evidence-backed badges are issued only from audit_product.

generate_viral_teardown

Generates conservative share copy without inventing TAM, live-scraping, source, or certification claims.

Running locally

python -m pip install "mcp>=1.0.0"
python server.py

Testing

The scoring core uses only the Python standard library.

python -m unittest discover -s tests -v
python -m py_compile core.py server.py

Methodology limitations

v2 validates evidence structure, URLs, source types, coverage, and deterministic scoring. It does not independently crawl the web or authenticate the contents behind a URL. An MCP host or researcher can gather the evidence first and pass it to audit_product.

A later version can add independent retrieval, source snapshots, signed audit receipts, appeals, and a public badge registry.

License

MIT © 2026 Oluwafemi Idiakhoa