Integration

NamSor integration

NamSor classifies personal names in any alphabet. Infer the likely gender of a name, its country of origin, the country of residence of its bearer, its region of origin within a country, and what kind of proper noun it is; split full names into first and last name; romanize, match and find candidate spellings of Chinese and Japanese names; and infer the dialing prefix of a phone number from its owner's name. Single-name tools and batch tools for up to 100 names. Each name consumes units (1 for gender, parsing and phone prefix, 10 for origin, residence and Indian state); list_service_costs gives the current costs. Not included: the religion, caste, US race/ethnicity, diaspora and community engagement classifiers (special-category personal data), corridor, preview, feedback and account administration routes. Authentication is an API key sent in the X-API-KEY header.

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When this happens, do that in NamSor.Built from the 66 NamSor tools below, on the connection you add once.

What you can do with NamSor

Each of these is one operation on one endpoint, available both as a workflow step and as a tool an AI agent can call on its own. Nothing bundles several operations into one action, so what a run did stays readable afterwards.

Most endpoints run on the free plan. A few, mainly the ones that publish on your behalf, need a paid plan.

Gender, 2 operations

infer_genderInfer genderGETInfers the likely gender of a person from a first and last name, with a gender scale from -1 (male) to +1 (female) and a calibrated probability. Uses 1 unit per name.
infer_gender_first_name_onlyInfer gender first name onlyGETInfers the likely gender from a first name alone, assuming a US context. NamSor recommends a full name (infer_gender) for better accuracy. Uses 1 unit per name.

Japanese name kanji candidate, 2 operations

list_japanese_kanji_candidatesList japanese kanji candidatesGETLists the likely Kanji spellings of a romanized Japanese name (for example Yamamoto Sanae), each with a probability. Uses 1 unit per name.
list_japanese_kanji_candidates_by_genderList japanese kanji candidates by genderGETLists the likely Kanji spellings of a romanized Japanese name, given the person's known gender (male or female). Uses 1 unit per name.

Parse name, 2 operations

parse_nameParse nameGETSplits an unsplit full name into first and last name, recognizing orders such as John Smith, SMITH, John or SMITH; John. Uses 1 unit per name.
parse_name_geoParse name geoGETSplits an unsplit full name into first and last name, using a country as local context for better accuracy. Uses 1 unit per name.

Api service, 1 operation

list_service_costsList service costsGETLists every NamSor classification service with the number of units it costs per name. Answers without consuming units.

Chinese name candidate, 1 operation

list_chinese_name_candidatesList chinese name candidatesGETLists the likely Chinese-character spellings of a romanized Chinese name (for example Wang Xiaoming), each with a probability. Uses 1 unit per name.

Chinese name candidates batch, 1 operation

list_chinese_name_candidates_batchList chinese name candidates batchPOSTLists the likely Chinese-character spellings of up to 100 romanized Chinese names at once. Uses 1 unit per name.

Chinese name candidates gender batch, 1 operation

list_chinese_name_candidates_by_gender_batchList chinese name candidates by gender batchPOSTLists the likely Chinese-character spellings of up to 100 romanized Chinese names at once, each with a known gender. Uses 1 unit per name.

Chinese name gender candidate, 1 operation

list_chinese_name_candidates_by_genderList chinese name candidates by genderGETLists the likely Chinese-character spellings of a romanized Chinese name, given the person's known gender (male or female). Uses 1 unit per name.

Chinese name match, 1 operation

match_chinese_nameMatch chinese nameGETScores how well a Chinese name in Chinese characters (for example 王晓明) matches a romanized name (for example Wang Xiaoming). Uses 1 unit per name.

Chinese name match batch, 1 operation

match_chinese_name_batchMatch chinese name batchPOSTScores up to 100 pairs at once, each a romanized Chinese name against a name in Chinese characters. Uses 1 unit per name.

Country, 1 operation

infer_residence_country_full_nameInfer residence country full nameGETInfers the likely country of residence of an unsplit full name or of a surname alone, with region, alternative and top 10 countries. Uses 10 units per name.

Country batch, 1 operation

infer_residence_country_full_name_batchInfer residence country full name batchPOSTInfers the likely country of residence of up to 100 unsplit full names or surnames at once. Uses 10 units per name.

Country fn ln, 1 operation

infer_residence_countryInfer residence countryGETInfers the likely country of residence of a first and last name, or of a surname alone, with region, alternative and top 10 countries. Uses 10 units per name.

Country fn ln batch, 1 operation

infer_residence_country_batchInfer residence country batchPOSTInfers the likely country of residence of up to 100 first and last names (or surnames) at once. Uses 10 units per name.

Gender batch, 1 operation

infer_gender_batchInfer gender batchPOSTInfers the likely gender of up to 100 first and last names at once, detecting the cultural context automatically. Uses 1 unit per name.

Gender chinese name, 1 operation

infer_gender_chinese_nameInfer gender chinese nameGETInfers the likely gender of a Chinese full name written in Chinese characters, for example 王晓明.

Gender chinese name batch, 1 operation

infer_gender_chinese_name_batchInfer gender chinese name batchPOSTInfers the likely gender of up to 100 Chinese full names written in Chinese characters at once.

Gender chinese name pinyin, 1 operation

infer_gender_chinese_pinyinInfer gender chinese pinyinGETInfers the likely gender of a Chinese name written in Pinyin (Latin script), for example Wang Xiaoming.

Gender chinese name pinyin batch, 1 operation

infer_gender_chinese_pinyin_batchInfer gender chinese pinyin batchPOSTInfers the likely gender of up to 100 Chinese names written in Pinyin at once.

Gender full, 1 operation

infer_gender_full_nameInfer gender full nameGETInfers the likely gender of an unsplit full name such as John H. Smith. Uses 1 unit per name.

Gender full batch, 1 operation

infer_gender_full_name_batchInfer gender full name batchPOSTInfers the likely gender of up to 100 unsplit full names at once. Uses 1 unit per name.

Gender full geo, 1 operation

infer_gender_full_name_geoInfer gender full name geoGETInfers the likely gender of an unsplit full name in a given country context (ISO2 code). Uses 1 unit per name.

Gender full geo batch, 1 operation

infer_gender_full_name_geo_batchInfer gender full name geo batchPOSTInfers the likely gender of up to 100 unsplit full names at once, each with its country context. Uses 1 unit per name.

Gender geo, 1 operation

infer_gender_geoInfer gender geoGETInfers the likely gender of a first and last name in a given country context (ISO2 code), which helps names whose gender varies by country. Uses 1 unit per name.

Gender geo batch, 1 operation

infer_gender_geo_batchInfer gender geo batchPOSTInfers the likely gender of up to 100 first and last names at once, each with its country context. Uses 1 unit per name.

Gender japanese name, 1 operation

infer_gender_japanese_nameInfer gender japanese nameGETInfers the likely gender of a Japanese name given as surname and given name in Latin script, for example Yamamoto Sanae.

Gender japanese name batch, 1 operation

infer_gender_japanese_name_batchInfer gender japanese name batchPOSTInfers the likely gender of up to 100 Japanese names given as surname and given name in Latin script, at once.

Gender japanese name full, 1 operation

infer_gender_japanese_full_nameInfer gender japanese full nameGETInfers the likely gender of an unsplit Japanese full name, for example 山本早苗.

Gender japanese name full batch, 1 operation

infer_gender_japanese_full_name_batchInfer gender japanese full name batchPOSTInfers the likely gender of up to 100 unsplit Japanese full names at once.

Japanese name gender kanji candidates batch, 1 operation

list_japanese_kanji_candidates_by_gender_batchList japanese kanji candidates by gender batchPOSTLists the likely Kanji spellings of up to 100 romanized Japanese names at once, each with a known gender. Uses 1 unit per name.

Japanese name kanji candidates batch, 1 operation

list_japanese_kanji_candidates_batchList japanese kanji candidates batchPOSTLists the likely Kanji spellings of up to 100 romanized Japanese names at once. Uses 1 unit per name.

Japanese name latin candidate, 1 operation

romanize_japanese_nameRomanize japanese nameGETLists the likely Latin-script (romaji) readings of a Japanese name written in Kanji, for example 山本早苗. Uses 1 unit per name.

Japanese name latin candidates batch, 1 operation

romanize_japanese_name_batchRomanize japanese name batchPOSTLists the likely Latin-script readings of up to 100 Japanese names written in Kanji at once. Uses 1 unit per name.

Japanese name match, 1 operation

match_japanese_nameMatch japanese nameGETScores how well a Japanese name in Kanji (for example 山本早苗) matches a romanized name (for example Yamamoto Sanae). Uses 1 unit per name.

Japanese name match batch, 1 operation

match_japanese_name_batchMatch japanese name batchPOSTScores up to 100 pairs at once, each a romanized Japanese name against a name in Kanji. Uses 1 unit per name.

Name type, 1 operation

classify_name_typeClassify name typeGETInfers what kind of proper noun a name is: a personal name, a brand, a place or another type, with the best alternative and a score.

Name type batch, 1 operation

classify_name_type_batchClassify name type batchPOSTInfers the kind of up to 100 proper nouns at once (personal name, brand, place...).

Name type geo, 1 operation

classify_name_type_geoClassify name type geoGETInfers what kind of proper noun a name is (personal name, brand, place...), using a country as local context.

Name type geo batch, 1 operation

classify_name_type_geo_batchClassify name type geo batchPOSTInfers the kind of up to 100 proper nouns at once, each with a country as local context.

Origin, 1 operation

infer_originInfer originGETInfers the likely country of origin of a first and last name, assuming it is spelled as in that country, with region, alternative and top 10 countries. NamSor advises other tools for melting-pot countries (US, CA, AU, NZ). Uses 10 units per name.

Origin batch, 1 operation

infer_origin_batchInfer origin batchPOSTInfers the likely country of origin of up to 100 first and last names at once. Uses 10 units per name.

Origin full, 1 operation

infer_origin_full_nameInfer origin full nameGETInfers the likely country of origin of an unsplit full name, assuming it is spelled as in that country, with region, alternative and top 10 countries. Uses 10 units per name.

Origin full batch, 1 operation

infer_origin_full_name_batchInfer origin full name batchPOSTInfers the likely country of origin of up to 100 unsplit full names at once. Uses 10 units per name.

Parse chinese name, 1 operation

parse_chinese_nameParse chinese nameGETSplits a Chinese name written in Chinese characters into surname and given name, for example 王晓明 into 王 and 晓明.

Parse chinese name batch, 1 operation

parse_chinese_name_batchParse chinese name batchPOSTSplits up to 100 Chinese names written in Chinese characters into surname and given name at once.

Parse japanese name, 1 operation

parse_japanese_nameParse japanese nameGETSplits a Japanese name, in Kanji (for example 山本早苗) or Latin script (Yamamoto Sanae), into surname and given name.

Parse japanese name batch, 1 operation

parse_japanese_name_batchParse japanese name batchPOSTSplits up to 100 Japanese names (Kanji or Latin script) into surname and given name at once.

Parse name batch, 1 operation

parse_name_batchParse name batchPOSTSplits up to 100 unsplit full names into first and last names at once. Uses 1 unit per name.

Parse name geo batch, 1 operation

parse_name_geo_batchParse name geo batchPOSTSplits up to 100 unsplit full names into first and last names at once, each with its country context. Uses 1 unit per name.

Phone code, 1 operation

infer_phone_prefixInfer phone prefixGETInfers the likely country and international dialing prefix of a phone number (formatted or not) from its owner's name, and returns it verified and normalized with libphonenumber. Uses 1 unit per name.

Phone code batch, 1 operation

infer_phone_prefix_batchInfer phone prefix batchPOSTInfers the likely country and dialing prefix of up to 100 phone numbers at once, from their owners' names. Uses 1 unit per name.

Phone code geo, 1 operation

infer_phone_prefix_geoInfer phone prefix geoGETInfers the likely dialing prefix of a phone number from the owner's name, given their country of residence (ISO2 code), and returns it verified and normalized. Uses 1 unit per name.

Phone code geo batch, 1 operation

infer_phone_prefix_geo_batchInfer phone prefix geo batchPOSTInfers the likely dialing prefix of up to 100 phone numbers at once, each with its owner's name and country of residence. Uses 1 unit per name.

Pinyin chinese name, 1 operation

romanize_chinese_nameRomanize chinese nameGETRomanizes a Chinese name written in Chinese characters to Pinyin, for example 王晓明 into Wang (surname) Xiaoming (given name).

Pinyin chinese name batch, 1 operation

romanize_chinese_name_batchRomanize chinese name batchPOSTRomanizes up to 100 Chinese names written in Chinese characters to Pinyin at once.

Subclassification, 1 operation

infer_regional_originInfer regional originGETInfers the likely region (state or province, as an ISO 3166-2 code) a first and last name comes from, within a given country.

Subclassification batch, 1 operation

infer_regional_origin_batchInfer regional origin batchPOSTInfers the likely region (ISO 3166-2 code) of up to 100 first and last names at once, each within its country.

Subclassification full, 1 operation

infer_regional_origin_full_nameInfer regional origin full nameGETInfers the likely region (state or province, as an ISO 3166-2 code) an unsplit full name comes from, within a given country.

Subclassification full batch, 1 operation

infer_regional_origin_full_name_batchInfer regional origin full name batchPOSTInfers the likely region (ISO 3166-2 code) of up to 100 unsplit full names at once, each within its country.

Subclassification indian, 1 operation

infer_indian_stateInfer indian stateGETInfers the likely Indian state or union territory (ISO 3166-2:IN code) of a first and last name. Uses 10 units per name.

Subclassification indian batch, 1 operation

infer_indian_state_batchInfer indian state batchPOSTInfers the likely Indian state or union territory (ISO 3166-2:IN code) of up to 100 first and last names at once. Uses 10 units per name.

Subclassification indian full, 1 operation

infer_indian_state_full_nameInfer indian state full nameGETInfers the likely Indian state or union territory (ISO 3166-2:IN code) of an unsplit full name. Uses 10 units per name.

Subclassification indian full batch, 1 operation

infer_indian_state_full_name_batchInfer indian state full name batchPOSTInfers the likely Indian state or union territory (ISO 3166-2:IN code) of up to 100 unsplit full names at once. Uses 10 units per name.

Use NamSor from Claude Code, Cursor or Codex

Connect NamSor in LiveContext, create an API key in Settings, then add the LiveContext MCP server to your client. Its NamSor operations become tools the client can call, next to every other integration you connected.

Claude Code

claude mcp add --transport http livecontext https://livecontext.ai/mcp --header "X-API-Key: YOUR_API_KEY"

Codex (~/.codex/config.toml)

[mcp_servers.livecontext]
url = "https://livecontext.ai/mcp"

[mcp_servers.livecontext.http_headers]
"X-API-Key" = "YOUR_API_KEY"

Cursor, Claude Desktop and other JSON configs

{
  "mcpServers": {
    "livecontext": {
      "type": "http",
      "url": "https://livecontext.ai/mcp",
      "headers": {
        "X-API-Key": "YOUR_API_KEY"
      }
    }
  }
}

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Add your NamSor connection in Settings. Every tool from this integration reuses it, and secrets are encrypted at rest and visible only to you.
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The workflow builds itself in front of you and picks the endpoints it needs. Drag the steps yourself on the canvas whenever you would rather be explicit.