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foundation-discovery

by io.github.Kindora-PBC

Search Funder Jobs

search_funder_jobs
Read-only

Search OPEN PHILANTHROPY JOBS at grantmaking foundations.

Surfaces roles involved in giving away money, running philanthropic programs, or executive leadership of philanthropic work. Backed by a weekly scrape of ~50K funder careers pages + GPT-5.4-mini classification against an 8-category taxonomy.

Categories (use the category param to filter):

  • grantmaking: Program officers, grants managers, RFP reviewers

  • program_leadership: VP Programs, Chief Program Officer, Program Director (cause-area)

  • executive_leadership: CEO, President, Executive Director at a foundation or community foundation

  • philanthropy_operations: Foundation finance/HR/IT/COO

  • program_support: Program associates, M&E officers, learning officers, program coordinators

  • development_for_grantmaking: Major gifts officers and development roles at community foundations and other regranting entities (NOT university or hospital development for the parent's operations)

  • philanthropy_communications: Foundation comms staff

  • philanthropy_strategy: Chief Strategy Officer, impact officer, equity & inclusion at a foundation

What's excluded by design: clinical/medical, retail, academic teaching, university advancement for the university itself, hospital fundraising for hospital ops, construction/facilities.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of results to return (1-50)
queryNoKeyword search on job title (case-insensitive substring). Examples: "program officer", "grants manager", "CEO", "communications director".
stateNo2-letter US state code (e.g., "CA", "NY"). Uses a broad SQL prefilter plus an exact token-aware post-filter against the location field.
remoteNoremote | hybrid | on-site.
countryNoFunder HQ country — ISO 3166-1 alpha-2 ("US", "GB", "DE") or a recognized name ("United Kingdom", "Germany"). Filters by where the FOUNDATION is headquartered; the corpus spans European funders, not just US. Independent of `state` (US foundations are the only ones with state-level location data).
sort_byNoResult ordering. 'funder_giving' (default) ranks by the foundation's annual average giving so the biggest funders surface first. 'recent' sorts by posted_at desc.funder_giving
categoryNoOne of the 8 philanthropy categories above.
seniorityNoExperience level: internship_fellowship | entry_level | mid | senior | executive. Comma-separate for multi-select — 'entry_level,internship_fellowship' is the early-career shelf (new grads, students). Rows not yet leveled by the classifier are excluded when set.
funder_einNoRestrict to one funder by EIN (9 digits, optional prefixes/dashes accepted).
funder_einsNoRestrict to a LIST of funder EINs (up to 100). The recommended compositional pattern is search_funders → search_funder_jobs(funder_eins=[...]) for cause-area searches (climate, racial equity, youth, etc.) where the cause isn't captured by the 8 role-family categories.
employment_typeNofull-time | part-time | contract | internship | fellowship | temporary.
exclude_categoriesNoCategories to hide. Default is no category exclusions; the public jobs board includes operations roles. Pass specific category names to suppress them.
posted_within_daysNoRecency window in days (0-730). Set 0 to disable.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful context beyond that: it explains the data pipeline (weekly scrape of ~50K pages) and the classification model (GPT-5.4-mini), plus explicit exclusions that set result expectations. This is useful behavioral info not present in annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: a clear opening sentence, a data-source note, a bulleted category list, and a concise exclusions list. While lengthy due to the category taxonomy, every section serves a distinct purpose—either clarifying parameter values or setting result expectations. Nothing is redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 13-parameter search tool with full schema coverage and an output schema, the description is remarkably complete. It covers domain boundaries, data freshness, classification methodology, category definitions, and explicit exclusions. The agent receives all necessary context to decide when and how to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% parameter description coverage, so it already carries much of the semantic load. The main description adds significant value by defining all 8 categories for `category`/`exclude_categories`, including concrete role examples and exclusions—information the schema references but does not enumerate. Other parameters are already well-documented in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource: 'Search OPEN PHILANTHROPY JOBS at grantmaking foundations.' It clearly identifies the tool's function and distinguishes it from sibling tools like search_funders (which searches funders) and search_open_grants (which searches grants).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context about scope and exclusions, including a detailed 'What's excluded by design' list and category definitions. However, it does not explicitly name alternative tools for other query types (e.g., 'for grants use search_open_grants'), so it stops short of full comparative guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.4/5.0
Disambiguation5/5

Each tool serves a distinct purpose: find_funders_for_peers uses peer-based collaborative filtering, search_funders does name/topic lookup, search_open_grants finds active RFPs, search_nonprofits searches recipients, get_funder_* tools provide different aspects of funder data, and utilities like health_check and list_tools are separate. No overlap.

Naming Consistency3/5

Naming conventions are mixed: 'find_', 'get_', 'search_', 'health_check', 'list_tools', 'get_ntee_codes'. While verbs are descriptive, there is no single consistent pattern (e.g., all search_ or all get_). This could cause slight confusion.

Tool Count5/5

12 tools is well-scoped for a foundation discovery server. Each tool addresses a clear need (prospecting, detail, financials, jobs, utilities) without being excessive or insufficient.

Completeness5/5

The tool set covers the full lifecycle of foundation research: finding funders (by peers, name, open grants), getting detailed profiles, grants, stats, financials, NTEE codes, and even funder jobs. The inclusion of recipient-side search (search_nonprofits) complements the funder side, leaving no obvious gaps.

Resources