Search Nonprofits (Grantee Benchmarking)
search_nonprofitsSearch the ~2M-org universe of US nonprofits (IRS Business Master File) — the RECIPIENT / grantee side, distinct from search_funders (the grantmaker side). Use this to benchmark peer organizations, research a cause's landscape, or find well-funded peers and then look up WHO funds them.
Each result carries 990 financials (revenue, program-expense ratio,
employees, governance score) and recipient-side funding signals:
total_grants_received (tracked foundation $ received), funder_count
(distinct funders), avg_grant_size_received, and funder_concentration_hhi.
CHOOSING THE RIGHT TOOL:
Want FUNDERS to apply to → use search_funders.
Want to research/benchmark RECIPIENT nonprofits (peers, "who received $X", landscape of a cause) → use search_nonprofits.
Found a well-funded peer and want their funders → note its
funder_count, then use get_foundation_grants / search_funders to find who funds that work.
THESIS MODE (funder-side sourcing): pass thesis, a plain-language description of
the organizations wanted ("early-stage nonprofits using AI to expand economic
mobility in the US"). The corpus is ranked by meaning instead of by name, and each
row carries similarity, technology_role / ai_usage (evidence-backed enums,
'unknown' when not yet assessed), evidence (verbatim quotes + source URLs), and the
organization's own Form 990 Part III text as mission_990 + programs (filing year, source link),
and funded_or_vetted_by: third-party facts with attribution and a link (grants a named funder
committed: count, years, total when every grant states one; challenge applications with status, where
'applicant' = applied only). unavailable_fields names any of these that could not be read for the page.
Combine with ruling_date_from (yyyymm; 201800 = ruled 2018+), ntee_codes, state,
metro, revenue / received bounds. Default sort in thesis mode is 'relevance';
'legibility_counterweight' adds a bounded bonus for small or lightly funded orgs.
Only organizations with a semantic profile are reachable in thesis mode; query
(name match) still reaches every organization.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | Case-insensitive exact city name. | |
| limit | No | Maximum number of results (1-50) | |
| metro | No | Colloquial metro/region ("Bay Area", "NYC", "Chicago", "Greater Boston") or a city name ("Cleveland", "Portland, OR") → resolved to its MSA codes. Prefer over city for metro-area intent. An unrecognized name does not error: the search runs without the metro filter and the result carries `geo_notice`. | |
| query | No | Case-insensitive substring match on the organization name. | |
| state | No | 2-letter US state code (e.g. "IL"). | |
| thesis | No | Plain-language description for thesis mode (see above). | |
| sort_by | No | Omit for the default. Lexical: received (default) | revenue | funders | name. Thesis mode: relevance (default) | legibility_counterweight | received. | |
| ai_usage | No | Thesis mode filter, same enum for AI specifically. Evidence-backed. | |
| msa_codes | No | Explicit CBSA codes if you already know them. | |
| ntee_codes | No | NTEE classification PREFIXES (e.g. ["K31"] food banks, ["E"] health, ["B20"] schools). Prefix match, so "K" = all food/agriculture, "K31" = food banks specifically. | |
| active_only | No | Only IRS-active orgs (default True). | |
| max_revenue | No | ||
| min_revenue | No | ||
| max_received | No | Maximum total received. | |
| min_received | No | Minimum total grant dollars RECEIVED (the key grantee-benchmarking filter — "received over $1M"). | |
| min_employees | No | Minimum employee count. | |
| technology_role | No | Thesis mode filter, subset of core | assisted | none | unknown (is technology the organization's PRODUCT?). Evidence-backed. | |
| min_funder_count | No | Minimum distinct funders. | |
| ruling_date_from | No | Thesis mode: earliest IRS ruling date as yyyymm. |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||