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wayanvota

Kindora-for-ChatGPT MCP server

by wayanvota

get_foundation_grants

Read-only

Retrieve a foundation's grants from its IRS 990-PF filings, filtered by year, recipient location, or purpose, with aggregate statistics.

Instructions

List individual grants a foundation has made, from its 990-PF filings.

Ordered largest-first with aggregate stats. Each grant reports recipient HQ country and US state when known. Note: recipient_country is where the grantee is registered, not necessarily where program work happens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
einYesFoundation EIN, 9 digits, with or without hyphen.
yearNoOptional filing year filter (e.g. 2023).
ntee_codeNoOptional NTEE code to filter recipients (e.g. "B41", "E").
recipient_countryNoISO 3166-1 alpha-2 recipient HQ country (e.g. "IN", "CH", "ZA").
recipient_stateNoTwo-letter US state code for recipient HQ.
purpose_keywordNoCase-insensitive substring matched against grant purpose (e.g. "vaccine", "digital health").
limitNoMax grants, 1-50 (default 20).
Behavior4/5

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

Annotations indicate readOnlyHint and openWorldHint. The description adds useful behavioral context: grants are ordered largest-first, include aggregate stats, and note the meaning of recipient_country. This goes beyond annotations without contradiction.

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 highly concise: two sentences plus a note, front-loaded with the main purpose. Every sentence adds value, with 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 tool has 7 parameters, no output schema, but good annotations, the description covers the main purpose, ordering, and data source. It lacks details on pagination or result limits, but the schema covers those, so it is largely complete.

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 each parameter already having a clear description. The tool description does not add any extra meaning or context about parameters beyond what the schema provides, earning baseline 3.

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 'List individual grants a foundation has made, from its 990-PF filings,' specifying the verb, resource, and data source. It also notes ordering and aggregate stats, distinguishing it from sibling tools like search_open_grants which search across foundations.

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 implies usage for listing grants from a specific foundation's filings but lacks explicit guidance on when to use this tool versus alternatives like search_open_grants. No exclusions or when-not-to-use are provided.

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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