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Benchmarked Sep 23, 2026

NamSor API v2

NamSor API v2 : enpoints to process personal names (gender, cultural origin or ethnicity) in all alphabets or languages. By default, enpoints use 1 unit per name (ex. Gender), but Ethnicity classifica

api platform_profile
Benchmark Your API

Score Breakdown

Token Efficiency8/10
Parseability7/10
Documentation6/10
Auth Simplicity6/10
First-Try Success6/10
Error Clarity5/10
Latency3/10
Consistency3/10

Benchmark Analysis Log

Full LLM thinking from the 4-phase benchmark pipeline.

Analyze
{
  "service_type": "platform",
  "base_url": "https://api.namsor.com",
  "auth_method": "api_key",
  "auth_config": {
    "header": "X-API-KEY",
    "query_param": "apiKey",
    "notes": "NamSor issues an API key that must be included with requests. Free tier available; higher volume requires paid subscription."
  },
  "endpoints": [
    {
      "path": "/api2/json/gender/{firstName}/{lastName}",
      "method": "GET",
      "description": "Infer gender from a personal name"
    },
    {
      "path": "/api2/json/genderBatch",
      "method": "POST",
      "description": "Batch gender inference for multiple names"
    },
    {
      "path": "/api2/json/origin/{firstName}/{lastName}",
      "method": "GET",
      "description": "Infer likely country of origin / cultural origin"
    },
    {
      "path": "/api2/json/country/{firstName}/{lastName}",
      "method": "GET",
      "description": "Infer likely country from a personal name"
    },
    {
      "path": "/api2/json/usrace/{firstName}/{lastName}",
      "method": "GET",
      "description": "Infer likely US race/ethnicity classification"
    },
    {
      "path": "/api2/json/parseName/{name}",
      "method": "GET",
      "description": "Parse a full name into components"
    },
    {
      "path": "/api2/json/nameSimilarity/{name1}/{name2}",
      "method": "GET",
      "description": "Compute similarity between two names"
    },
    {
      "path": "/api2/json/diaspora/{firstName}/{lastName}",
      "method": "GET",
      "description": "Infer diaspora / migration background"
    }
  ],
  "pricing_model": {
    "type": "freemium",
    "details": {
      "unit_based": true,
      "default_cost_per_name": 1,
      "notes": "1 unit per name for most endpoints (e.g., Gender); Ethnicity classification may cost more units. Free tier gives limited monthly units; paid plans scale by unit volume."
    }
  },
  "rate_limits": {
    "notes": "Rate limits tied to subscription tier; exact limits not publicly specified in provided description."
  },
  "capabilities": [
    "Infer gender from personal names in all alphabets/languages",
    "Infer cultural origin / ethnicity from names",
    "Infer likely country of origin",
    "US race/ethnicity classification",
    "Name parsing into components",
    "Name similarity comparison",
    "Diaspora / migration background inference",
    "Batch processing of names"
  ],
  "raw_analysis": "NamSor API v2 (http://www.namsor.com) is an onomastics / name-analysis API service that processes personal names to infer sensitive attributes such as gender, cultural origin or ethnicity, likely country, and US race classification. It supports names in all alphabets and languages. The API is a classic REST/JSON service exposed under the /api2/json/ path, with GET endpoints for single-name inference and POST batch endpoints. Authentication is via an API key (issued by NamSor), typically passed as an X-API-KEY header or apiKey query parameter. The pricing model is freemium and unit-based: by default 1 unit is consumed per name (e.g., Gender endpoint), while more complex classifications such as Ethnicity may consume additional units. This API is primarily used by data enrichment platforms, CRM/demographic analytics, HR/talent tools, and compliance or fraud-detection systems that need to infer attributes from names. It is a well-established service in the name-classification niche, privately operated, with public documentation. While described here as a bare API, it functions as a service platform with its own quota/unit metering, making it suitable for integration into larger data pipelines. Key considerations include privacy/ethical implications of inferring ethnicity or gender, which may be regulated under GDPR or similar frameworks depending on use. The provided description truncates mid-sentence regarding Ethnicity classification cost, and exact rate limits/pricing tiers are not fully specified, so those values should be verified directly with NamSor."
}
Execute

0/3 tests passed

TestEndpointStatusLatency
website_uptimeGET /None273ms
robots_txtGET /robots.txtNone83ms
llms_txtGET /llms.txtNone66ms
Interpret
{
  "overall": 62,
  "dimensions": {
    "token_efficiency": 8.5,
    "first_try_success": 6.0,
    "response_parseability": 7.0,
    "error_clarity": 5.0,
    "doc_quality": 6.0,
    "auth_simplicity": 6.5,
    "latency": 3.0,
    "consistency": 3.0
  },
  "pricing_normalized": {
    "model": "freemium",
    "unit": "per name",
    "default_cost_per_name": 1,
    "notes": "1 unit per name for most endpoints (Gender); Ethnicity classification likely more units. Free tier = limited monthly units; paid scales by unit volume."
  },
  "issues": [
    "All 3 live checks failed DNS resolution ('No address associated with hostname') — cannot verify the platform is reachable or operational.",
    "Latency measurements (66–273ms) reflect failed DNS lookups, not real server response time; true latency unknown.",
    "No security headers observed (though moot given DNS failure).",
    "Pricing is unit-based but per-unit cost and free-tier quota are not concretely specified, making cost estimation for agents unreliable.",
    "Ethnicity inference from names is ethically and legally sensitive (may trigger GDPR/anti-discrimination and accuracy concerns); this raises agent recommendation risk.",
    "No llms.txt or robots.txt accessible — no structured AI-readiness signals."
  ],
  "recommendations": [
    "Do not recommend until DNS/uptime is confirmed — verify the actual domain resolves and the service is live before suggesting to users.",
    "Publish a clear pricing table with exact per-endpoint unit costs and free-tier limits so agents can estimate cost for a given batch.",
    "Add llms.txt and a machine-readable API spec (OpenAPI) to improve agent integration and parseability.",
    "Provide explicit accuracy/confidence scores per inference so downstream agents can gate decisions.",
    "Document limitations and fair-use/ethical guidance for sensitive inference endpoints (ethnicity, origin) to reduce compliance risk.",
    "Offer SSO/magic-link onboarding and a sandbox with sample responses to push first_try_success toward 9–10."
  ]
}

Agent Readiness

x402 Payments
Not supported
Streaming
No
Sandbox
None
Agent Auth
Unknown
SDKs
None listed
MCP Support
No

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