DataForB2B

ai.dataforb2b/dataforb2b

This server connects Claude to DataForB2B's 800M profile and 75M company database through their MCP endpoint. You get search operations with 60+ people filters (title, company size, location, open_to_work status) and 50+ company filters (funding, industry, size), plus enrichment calls that turn LinkedIn URLs or domains into structured contact data with verified emails. It's built for agents doing prospecting, sourcing, or lead qualification where you need to resolve decision makers at a company, build dynamic ICP lists, or enrich profiles on demand. The underlying API is credit-based REST with webhook support, and this MCP wrapper exposes those same capabilities as native tools Claude can call directly.

tools: 6
transport: HTTP
last updated: May 14, 2026

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Tools

6 tools

Public tool metadata for what this MCP can expose to an agent.

search_natural_languageSearch people or companies using a free-form English query. The backend LLM interprets the query and builds structured filters automatically. Use this for fuzzy intent like "software engineers in Paris with 5+ years" or "AI startups in France that raised Series A". For determi...5 params

Search people or companies using a free-form English query. The backend LLM interprets the query and builds structured filters automatically. Use this for fuzzy intent like "software engineers in Paris with 5+ years" or "AI startups in France that raised Series A". For determi...

Parameters* required

categorystring

"people" to search professional profiles, "company" to search organizations.one of people · companydefault: people

enrich_liveboolean

When true, results are enriched live from LinkedIn (fresher, costs more credits). When false, results are served from the cached database (faster, cheaper).default: true

pageinteger

Page number, 1-indexed.default: 1

page_sizeinteger

Number of results per page (max 100).default: 25

querystring

Natural language search query (min 3 chars). Examples: "Software engineers in Paris with 5+ years of experience", "AI startups in France that raised Series A", "Marketing managers at companies with...

search_peopleSearch professional profiles using structured filters. Use this when you want precise, deterministic filtering on specific columns (exact country, company size range, funding stage, etc.). For free-form queries like "AI engineers in Berlin who recently raised", prefer `search_...7 params

Search professional profiles using structured filters. Use this when you want precise, deterministic filtering on specific columns (exact country, company size range, funding stage, etc.). For free-form queries like "AI engineers in Berlin who recently raised", prefer `search_...

Parameters* required

countinteger

Number of profiles to return (1-100 recommended; max 5000).default: 25

enrich_liveboolean

When true (1.5 credits/profile), each profile is enriched live from LinkedIn so data is fresh. When false (0.75 credits/profile), data is served from the cached database — faster and cheaper.default: true

filtersobject

FilterGroup with shape {"op": "and"|"or", "conditions": [...]}. Each condition is {"column": "<name>", "type": "<operator>", "value": <value>, "value2": <optional, for between>}. Groups can be nest...

offsetinteger

Pagination offset (number of results to skip).default: 0

order_byvalue

Column to sort by, e.g. "follower_count", "years_of_experience".

order_directionstring

Sort direction.one of asc · descdefault: desc

reveal_personal_emailboolean

When true, the actual personal_emails array is included on each result (extra credits per email revealed). When false, only the has_personal_email boolean flag is returned.default: false

search_companySearch companies using structured filters. Use this when you want precise filtering on specific columns (industry, country, employee count range, funding stage, founded year, etc.). For free-form queries like "AI startups in France that raised Series A", prefer `search_natural...6 params

Search companies using structured filters. Use this when you want precise filtering on specific columns (industry, country, employee count range, funding stage, founded year, etc.). For free-form queries like "AI startups in France that raised Series A", prefer `search_natural...

Parameters* required

countinteger

Number of companies to return (1-100 recommended; max 1000).default: 25

enrich_liveboolean

When true (1.5 credits/company), each company is enriched live from LinkedIn so data is fresh. When false (0.75 credits/company), data is served from the cached database — faster and cheaper.default: false

filtersobject

offsetinteger

Pagination offset (number of results to skip).default: 0

order_byvalue

Column to sort by, e.g. "follower_count", "employee_count", "founded_year", "last_funding_amount_usd".

order_directionstring

Sort direction.one of asc · descdefault: desc

enrich_profileEnrich a single professional profile with detailed profile data, work email, personal email, and/or GitHub profile. Each enrichment flag controls one data source and bills separately.5 params

Enrich a single professional profile with detailed profile data, work email, personal email, and/or GitHub profile. Each enrichment flag controls one data source and bills separately.

Parameters* required

enrich_githubboolean

Find the person's GitHub profile URL.default: false

enrich_personal_emailboolean

Find the person's personal email.default: false

enrich_profileboolean

Return the full profile object (experience, education, skills, etc.).default: true

enrich_work_emailboolean

Find the person's professional / work email.default: false

profile_identifierstring

The profile to enrich. Accepts any of: a LinkedIn URL (e.g. "https://linkedin.com/in/john-doe"), a public_id slug (e.g. "john-doe"), or an encoded DataForB2B ID (e.g. "prof_xxx").

enrich_companyEnrich a single company with full data from public sources (description, industry, headquarters, employee count, funding, offices, etc.).1 params

Enrich a single company with full data from public sources (description, industry, headquarters, employee count, funding, offices, etc.).

Parameters* required

company_identifierstring

The company to enrich. Accepts any of: a universal_name slug (e.g. "google"), a LinkedIn company URL (e.g. "https://linkedin.com/company/google"), or an encoded DataForB2B ID (e.g. "org_xxx").

search_lookalikeFind similar people or companies using AI-powered vector similarity. Provide either `profile` (to find similar people) OR `company` (to find similar companies) — not both. Optionally narrow results with `country` or `location`.7 params

Find similar people or companies using AI-powered vector similarity. Provide either `profile` (to find similar people) OR `company` (to find similar companies) — not both. Optionally narrow results with `country` or `location`.

Parameters* required

companyvalue

Seed company to find lookalikes for. Accepts a LinkedIn company URL or universal_name slug. Mutually exclusive with `profile`.

countinteger

Number of results to return.default: 25

countryvalue

Restrict results to a country. ISO-2 code (e.g. "FR", "US", "GB").

enrich_liveboolean

When true, results are enriched live from LinkedIn (fresher, costs more credits). When false, results are served from the cached database.default: true

locationvalue

Restrict results to a free-text location, e.g. "Paris" or "San Francisco Bay Area".

offsetinteger

Pagination offset.default: 0

profilevalue

Seed profile to find lookalikes for. Accepts a LinkedIn URL, public_id slug, or member_identity. Mutually exclusive with `company`.

Details

Website: dataforb2b.ai
Categories: Sales & Marketing, Search & Web Crawling
Transport: HTTP
Registry: MCP registry, active

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