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MCPToolsFind Companies

Find Companies

Describe the company profile you want, get back a list of matching companies.

Tool
find_companies
Cost
0.3 credits per result
Category
Company Search
Required input
None — pass at least one filter
Returns
Matching company profiles

What it does

  • Searches for companies across Crustdata and returns matching company profiles.
  • No required parameter. You must pass at least one filter or there is nothing to search on.
  • Filters cover identity, category, location, type, size, revenue, funding, social following, age and headcount growth — see the tables below.
  • Paged: limit is 1–100 (default 25), and cursor takes the next_cursor from the previous response.
  • Cost scales with results — 0.3 credits per company returned. A default page of 25 costs 7.5 credits; a full page of 100 costs 30.

This is how you build an account list. Enrichment tools like enrich_organization work on domains you already have — this call produces them.


Parameters

No parameter is required, but a call with no filters has nothing to match on. Combine filters from any of the families below.

Identity

ParameterTypeNotes
company_namesarrayFuzzy, any-of
primary_domainsarrayExact

Category

ParameterTypeNotes
industriesarray
exclude_industriesarrayNone-of
categoriesarray
specialitiesarrayLinkedIn specialities

Location

ParameterTypeNotes
countriesarrayExact HQ country
headquarters_locationsarrayFuzzy
largest_headcount_countriesarrayWhere most employees sit

Type

ParameterTypeNotes
company_typesarraye.g. "Public Company", "Privately Held"
marketsarraye.g. "NYSE", "NASDAQ", or "PRIVATE"

Size

ParameterTypeNotes
employee_count_rangesarray"1-10" | "11-50" | "51-200" | "201-500" | "501-1000" | "1001-5000" | "5001-10000" | "10001+"
min_headcountnumber
max_headcountnumber

Revenue

ParameterTypeNotes
min_revenue_usdnumber
max_revenue_usdnumber

Funding

ParameterTypeNotes
min_total_funding_usdnumber
max_total_funding_usdnumber
last_funding_round_typesarraye.g. "seed", "series_a", "series_b"
min_last_round_amount_usdnumber
max_last_round_amount_usdnumber
last_fundraise_afterstringYYYY-MM-DD
last_fundraise_beforestringYYYY-MM-DD
investorsarray

Social

ParameterTypeNotes
min_followersnumberLinkedIn follower count
max_followersnumberLinkedIn follower count

Age

ParameterTypeNotes
min_year_foundednumber
max_year_foundednumber

Growth

ParameterTypeNotes
min_headcount_growth_percent_1mnumberPercent growth over 1 month
min_headcount_growth_percent_3mnumberPercent growth over 3 months
min_headcount_growth_percent_6mnumberPercent growth over 6 months
min_headcount_growth_percent_12mnumberPercent growth over 12 months
min_headcount_growth_absolute_1mnumberHeadcount added over 1 month
min_headcount_growth_absolute_3mnumberHeadcount added over 3 months
min_headcount_growth_absolute_6mnumberHeadcount added over 6 months
min_headcount_growth_absolute_12mnumberHeadcount added over 12 months

Paging

ParameterTypeNotes
limitnumber1–100, default 25
cursorstringFrom next_cursor on the previous response

How to use it

Connect the MCP server

Follow the setup guide for your client. Browser sign-in, no API key.

Describe the account profile

Find privately held SaaS companies in the US with 51-200 employees.

Add the timing signal

Narrow that to companies whose last funding round was series_a or series_b and closed after 2025-06-01.

Add the growth signal

Now only keep companies with min_headcount_growth_percent_6m above 20.

Page through the results

Return 100 per page and keep paging with the cursor until I have 300 companies. Report credits used.

Example prompts

Ask your clientWhat you get back
”Privately held fintech in Germany, 201-500 employees”Companies matching company_types, industries, countries, employee_count_ranges
”Series A companies that raised after 2025-06-01”last_funding_round_types plus last_fundraise_after
”Companies that grew headcount 20%+ in the last 6 months”min_headcount_growth_percent_6m
”Founded after 2020, over $5M raised, not in retail”min_year_founded, min_total_funding_usd, exclude_industries
”NASDAQ-listed companies with over 100k LinkedIn followers”markets plus min_followers

Best prompting practices

  • Always pass at least one filter. There is no required parameter, so an unfiltered ask returns whatever the search defaults to rather than what you meant.
  • Use the range enums, not free text. employee_count_ranges accepts fixed buckets — "51-200", "1001-5000". “Mid-market” is not a value.
  • Dates are YYYY-MM-DD. last_fundraise_after: "2025-06-01". “Last six months” will not parse.
  • Stack growth windows deliberately. min_headcount_growth_percent_1m catches a sudden spike; _12m catches sustained expansion. Percent filters favour small companies, absolute filters favour large ones.
  • Exclude early. exclude_industries costs nothing and keeps agencies, recruiters and consultancies out of a list you would otherwise clean by hand.
  • Set limit explicitly. Default is 25. limit is the cost dial at 0.3 credits per company, so ask for volume only when you will use it.
  • Search before you enrich. 100 companies here is 30 credits; guessing 100 domains and enriching them at 2 credits each is 200.

Output

{ "companies": [ { "company_name": "Example Inc", "primary_domain": "example.com", "employee_count_range": "51-200", "countries": ["United States"] } ], "next_cursor": "eyJwYWdlIjoyfQ" }

Credits and limits

0.3 credits per company returned. limit is 1–100 with a default of 25, so a default call costs 7.5 credits and a full page costs 30. Run check_credits (free) before a long paging run.

The cost is per result, so paging is what drives spend. Three pages of 100 is 300 companies and 90 credits. Tell your client how many companies you want up front rather than letting it page until the cursor runs out.


ToolUse it instead when
find_peopleYou want people across many companies, not the companies themselves — 0.3 credits per result
enrich_organizationYou already have the domains and need firmographics — 2 credits
enrich_linkedin_pageYou already have LinkedIn pages and want the cheapest enrichment — 0.5 credits
find_people_within_companyYou have your account list and now need contacts at each one — 0.3 credits
google_maps_listingsYou are sourcing local businesses rather than firmographic B2B accounts — 0.3 credits

Next steps

  • All MCP tools — the full catalogue with credit costs
  • TAM Sourcing — workflows that turn a company search into a working list

Keep learning

Two courses take these tools past the reference page — clustered by GTM job, then chained into workflows.