# MCP: Grant & Foundation Funding Intel - AI Agents (`seibs.co/mcp-grant-funding-intel`) Actor MCP server for grant-funding-intel. Four AI-agent tools: search\_grants (federal opportunities), find\_funders (990-PF foundations), profile\_funder (giving history + grant awards), match\_funders (scored likely funders). For grant-writing AI agents. x402 (USDC on Base) and Skyfire ready. - **URL**: https://apify.com/seibs.co/mcp-grant-funding-intel.md - **Developed by:** [Seibs.co](https://apify.com/seibs.co) (community) - **Categories:** AI, Business, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $5.00 / 1,000 mcp tool calls This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## MCP: Grant & Foundation Funding Intel **Model Context Protocol (MCP) server** that gives AI agents grant and foundation-funding intelligence as structured tools. Wraps the [`grant-funding-intel`](https://apify.com/seibs.co/grant-funding-intel) actor, which unifies federal grant opportunities (Grants.gov) and private-foundation giving (IRS 990-PF via ProPublica) into one normalized funding schema. Built for **grant-writer, nonprofit, and fundraising AI agents**. --- ### Tools | Tool | What it does | |---|---| | `search_grants` | Open federal grant opportunities by keyword, eligibility, and funding category - award range, eligibility, focus, deadline. | | `find_funders` | Private foundations by name / state / focus area, with multi-year giving history (grants paid, payout ratio, asset trend). | | `profile_funder` | Deep profile one foundation by EIN: giving history, payout vs the 5% minimum, and recipient-level grant awards (when a 990-PF XML URL is supplied). | | `match_funders` | Surface + score the funders most likely to fund a cause - federal opportunities and foundations ranked with transparent reasons. | --- ### Usage **Discover the tools (free):** ```json { "mode": "list_tools" } ```` **Find open education grants for nonprofits:** ```json { "mode": "call_tool", "tool": "search_grants", "args": { "keyword": "literacy", "category": ["education"], "eligibility": ["nonprofits_with_501c3"], "limit": 15 } } ``` **Match funders to a project:** ```json { "mode": "call_tool", "tool": "match_funders", "args": { "cause": "after-school literacy in rural Ohio", "focus_area": "Education", "state": "OH", "min_assets": 5000000 } } ``` **Batch several tools in one run:** ```json { "mode": "batch", "calls": [ { "tool": "search_grants", "args": { "keyword": "housing" } }, { "tool": "find_funders", "args": { "state": "CA", "focus_area": "Education" } } ] } ``` Every tool returns `{ tool, args, ok, count, items, summary, error }`. Agents key off `ok` + `summary` + `items`. *** ### Pricing Flat **$0.005 per MCP tool call** (`mcp_tool_call`). The upstream `grant-funding-intel` actor charges its own events to the same run as pass-through: `funder_record` $0.006, `grant_history_enrichment` $0.012, `funder_match` $0.015. #### Agentic payments (x402 + Skyfire) This server is **x402 (USDC on Base)** and **Skyfire** ready. When the operator enables MCP monetization, an AI agent can pay per tool call with no pre-provisioned API token. `mode=list_tools` returns the payment descriptor (accepted rails, price, wallet/seller identity) so agents know they can call pay-per-use. Configure via environment: `X402_ENABLED`, `X402_PAY_TO_ADDRESS`, `X402_PRICE_USD`, `SKYFIRE_ENABLED`, `SKYFIRE_SELLER_ID`. *** ### Sources & responsible use Grants.gov (federal opportunities) and IRS Form 990 / 990-PF via ProPublica Nonprofit Explorer (foundation giving) - public APIs, no keys, logged-out. PII minimized; public-record names only. Verify deadlines and eligibility against the official source before applying. See the upstream actor's README for full source and legal-safety detail. # Actor input Schema ## `mode` (type: `string`): list\_tools = emit the MCP tool catalog (free). call\_tool = invoke one tool (requires 'tool' + 'args'). batch = invoke a list of {tool, args} calls. ## `tool` (type: `string`): Required when mode=call\_tool. One of: search\_grants, find\_funders, profile\_funder, match\_funders. ## `args` (type: `object`): Arguments for the selected tool. Example for search\_grants: {"keyword": "literacy", "category": \["education"], "limit": 15}. ## `calls` (type: `array`): Required when mode=batch. Each entry is an object with 'tool' and 'args'. Example: \[{"tool": "search\_grants", "args": {"keyword": "housing"}}, {"tool": "find\_funders", "args": {"state": "CA", "focus\_area": "Education"}}]. Max 10 calls per run. ## Actor input object example ```json { "mode": "list_tools", "tool": "search_grants", "args": { "keyword": "literacy", "category": [ "education" ], "limit": 15 }, "calls": [] } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "mode": "list_tools", "args": { "keyword": "literacy", "category": [ "education" ], "limit": 15 } }; // Run the Actor and wait for it to finish const run = await client.actor("seibs.co/mcp-grant-funding-intel").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "mode": "list_tools", "args": { "keyword": "literacy", "category": ["education"], "limit": 15, }, } # Run the Actor and wait for it to finish run = client.actor("seibs.co/mcp-grant-funding-intel").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "mode": "list_tools", "args": { "keyword": "literacy", "category": [ "education" ], "limit": 15 } }' | apify call seibs.co/mcp-grant-funding-intel --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seibs.co/mcp-grant-funding-intel", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MCP: Grant & Foundation Funding Intel - AI Agents", "description": "MCP server for grant-funding-intel. Four AI-agent tools: search_grants (federal opportunities), find_funders (990-PF foundations), profile_funder (giving history + grant awards), match_funders (scored likely funders). For grant-writing AI agents. x402 (USDC on Base) and Skyfire ready.", "version": "0.1", "x-build-id": "awGpZziqU08vINLub" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seibs.co~mcp-grant-funding-intel/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seibs.co-mcp-grant-funding-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seibs.co~mcp-grant-funding-intel/runs": { "post": { "operationId": "runs-sync-seibs.co-mcp-grant-funding-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seibs.co~mcp-grant-funding-intel/run-sync": { "post": { "operationId": "run-sync-seibs.co-mcp-grant-funding-intel", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "list_tools", "call_tool", "batch" ], "type": "string", "description": "list_tools = emit the MCP tool catalog (free). call_tool = invoke one tool (requires 'tool' + 'args'). batch = invoke a list of {tool, args} calls.", "default": "list_tools" }, "tool": { "title": "Tool name", "enum": [ "search_grants", "find_funders", "profile_funder", "match_funders" ], "type": "string", "description": "Required when mode=call_tool. One of: search_grants, find_funders, profile_funder, match_funders.", "default": "search_grants" }, "args": { "title": "Tool arguments (JSON object)", "type": "object", "description": "Arguments for the selected tool. Example for search_grants: {\"keyword\": \"literacy\", \"category\": [\"education\"], \"limit\": 15}." }, "calls": { "title": "Batch calls", "maxItems": 10, "type": "array", "description": "Required when mode=batch. Each entry is an object with 'tool' and 'args'. Example: [{\"tool\": \"search_grants\", \"args\": {\"keyword\": \"housing\"}}, {\"tool\": \"find_funders\", \"args\": {\"state\": \"CA\", \"focus_area\": \"Education\"}}]. Max 10 calls per run.", "default": [] } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```