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wayanvota

Kindora-for-ChatGPT MCP server

by wayanvota

search_funders

Read-only

Find grantmaking foundations by name, cause area, or location. Searches over 200,000 US and European funders from IRS 990 data to identify aligned funders for relationship-based grants.

Instructions

Find grantmaking organizations by name, cause area, or location.

Searches 174K+ US foundations plus 32K+ European funders from IRS 990 data. Use this to find aligned funders (including ones that fund by relationship, LOI, or annual cycle rather than a live RFP), or when the user names a specific funder. For active/open opportunities use search_open_grants instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoFunder name or cause-area phrase. Topic searches work best with 2+ words (e.g. "Ford Foundation", "global health", "community foundation").
stateNoTwo-letter US state code for funder HQ (e.g. "CA", "NY").
cityNoCity name for funder HQ (case-insensitive).
ntee_codeNoNTEE classification code to filter by (e.g. "E" Health, "B" Education).
min_assetsNoMinimum total assets in dollars (e.g. 10000000).
max_assetsNoMaximum total assets in dollars.
has_er_grantsNoIf true, only funders that make expenditure-responsibility grants (to non-501(c)(3) entities like PBCs, for-profits, foreign orgs).
funder_typeNoNarrow to one type, e.g. "community_foundation", "family_foundation", "corporate_foundation", "private_operating", "independent_foundation".
exclude_funder_typesNoTypes to hide, e.g. ["operating_nonprofit"] to drop orgs that surface with large annual_grants but are not really grantmakers.
countryNoOne or more funder HQ countries for non-US funders (e.g. ["Germany", "Spain", "Netherlands"]).
grantee_country_codesNoISO 3166-1 alpha-2 codes for where the funder's grantees are based (e.g. ["IN"], ["KE", "SF"]).
limitNoMax results, 1-50 (default 20).
Behavior4/5

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

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds that it searches 174K+ US and 32K+ European funders from IRS 990 data, implying a broad but possibly stale dataset. It does not mention pagination or exact return format, but overall provides useful behavioral context beyond 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 concise with two short paragraphs. The first sentence is a clear verb+resource statement. Every sentence serves a purpose: stating what it does, providing data source, and giving usage guidance with sibling differentiation. No wasted words.

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

Completeness4/5

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

Given the complexity (12 parameters) and lack of output schema, the description covers the core purpose, data sources, and when to use. It could mention output format (list of funders with details) for completeness, but the absence is minor given the clarity otherwise.

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

Parameters3/5

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

Schema coverage is 100% with detailed descriptions for all 12 parameters. The description adds no additional parameter-specific meaning beyond the schema, so the baseline of 3 applies.

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 clearly states the tool finds grantmaking organizations by name, cause area, or location. It also distinguishes from the sibling tool search_open_grants by specifying that search_funders is for funders that may not have a live RFP, while the sibling is for active opportunities.

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

Usage Guidelines5/5

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

The description explicitly provides when to use (to find aligned funders, or when user names a specific funder) and when not to use (for active/open opportunities, use search_open_grants instead). It also adds context that these funders may fund by relationship, LOI, or annual cycle.

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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