Kindora-for-ChatGPT MCP server

A Python FastMCP server that re-exposes Kindora's public funder and grant tools so you can add them to ChatGPT as a custom MCP connector (Developer mode / Apps). It is a thin proxy: every tool forwards to the live Kindora MCP endpoint and returns the result unchanged.

Easiest install: deploy, then paste the URL into ChatGPT

ChatGPT can't run a local repo; it connects to a hosted HTTPS URL. This server needs no API keys (Kindora's free tier is open), so hosting it is one click, no Terminal, no .env.

1. Click the button, sign in to Render with GitHub, and approve. Render reads render.yaml and builds the container. First deploy takes ~2 minutes.

2. Copy the service URL Render gives you and add /mcp to the end, e.g. https://kindora-chatgpt-mcp.onrender.com/mcp.

3. In ChatGPT: Settings -> Connectors (Apps) -> Advanced -> Developer mode, then Create connector, paste the URL, choose No authentication, and save. Enable it from the "+" menu in any chat.

That's the whole install. (Render's free tier sleeps when idle, so the first request after a quiet spell takes 30-60 seconds to wake. Fly.io, below, stays warmer.)

What the Kindora MCP does

Kindora is a grant-discovery platform for nonprofits. Its MCP server is a read-only, free-tier service over public IRS 990 / 990-PF filings and Grants.gov opportunities, covering 174K+ US foundations, 32K+ European funders, and 43K+ open grants. The upstream server exposes ten tools across discovery, profile, financials, and reference categories. This wrapper mirrors the nine that matter for ChatGPT (it drops the upstream list_tools helper, which is redundant since MCP clients enumerate tools natively).

All tools are annotated readOnlyHint: true, so ChatGPT runs them without write-confirmation prompts.

Why wrap it instead of pointing ChatGPT at Kindora directly?

ChatGPT Developer mode can connect to any remote MCP server, so a wrapper is optional. It earns its place when you want one endpoint you control: curated tool descriptions, read-only annotations, a place to add auth / rate limiting / logging, header injection for a Kindora key, or to pin a specific upstream URL. If you want none of that, you can skip this and add https://mcp.kindora.co/mcp to ChatGPT directly.

Repository contents

.
├── server.py            # the MCP server (all nine tools)
├── requirements.txt     # runtime dependency (fastmcp)
├── pyproject.toml        # packaging + pytest config
├── test_server.py        # offline tests (no network needed)
├── Dockerfile            # container image for hosting
├── fly.toml              # one-command deploy to Fly.io
├── render.yaml           # one-click deploy to Render
├── .env.example          # configuration reference
├── .github/workflows/    # CI (runs the tests)
├── LICENSE               # MIT
└── README.md

Requirements

• Python 3.10+

• A way to host a public HTTPS URL if you want to use it with ChatGPT (Docker + any host, or the included Fly/Render configs).

Get the code

git clone https://github.com/wayanvota/kindora-chatgpt-mcp.git
cd kindora-chatgpt-mcp
pip install -r requirements.txt

Run locally (stdio)

python server.py

Use this for local testing or to add the server to a stdio MCP client (e.g. Claude Desktop).

Run the tests

pip install -e ".[dev]"
pytest

The suite is offline: it checks tool registration, the read-only annotations, and the upstream-response normalization with a stubbed client. It does not make a live call to Kindora.

Run as a public HTTP endpoint (required for ChatGPT)

ChatGPT connects to a publicly reachable HTTPS URL, so the server must run with the streamable-HTTP transport behind a public host.

TRANSPORT=http HOST=0.0.0.0 PORT=8000 python server.py
# serves MCP at http://<host>:8000/mcp

Or with Docker:

docker build -t kindora-chatgpt-mcp .
docker run -p 8000:8000 kindora-chatgpt-mcp

Put it behind TLS (a reverse proxy, or a platform like Fly.io / Render / Railway / Cloud Run) so the final URL is https://your-domain/mcp.

One-click hosting

This repo ships configs for two free-tier hosts that give you a public HTTPS URL:

Fly.io (fly.toml):

fly launch --no-deploy --copy-config --name <your-app>
fly deploy
# URL: https://<your-app>.fly.dev/mcp

Render (render.yaml): push the repo to GitHub, then in Render choose New + -> Blueprint and pick the repo. URL: https://<service>.onrender.com/mcp.

Connect it to ChatGPT

1. Enable Developer mode: Settings -> Connectors (Apps) -> Advanced -> Developer mode. (Available on Plus, Pro, Business, Enterprise, and Edu plans.)

2. Connectors -> Create / Add custom connector.

3. Enter your public URL, e.g. https://your-domain/mcp.

4. Authentication: No authentication (this proxy and Kindora's free tier are open). Add OAuth here only if you put your own auth in front.

5. Save, then enable the connector in a chat (the "+" / tools menu in the composer).

Developer mode does not require the search/fetch compatibility tools that ChatGPT's Deep Research connectors need; it accepts arbitrary named tools, which is why all nine Kindora tools are exposed directly.

Configuration

All via environment variables (see .env.example):

Confirm the upstream URL against your Kindora connector listing. If Kindora moves the endpoint, set KINDORA_MCP_URL and nothing else changes.

Notes and limits

• Read-only / public data. Nothing here writes. Data is public IRS 990 and Grants.gov.

• Rate limits. Kindora's free tier is roughly 100 requests/hour for anonymous users; the proxy inherits that.

• Country fields in grant data reflect the recipient's HQ country, not where program work is implemented.

• Verify before publishing facts. Treat tool output as leads, not citations: confirm a foundation's current giving and open RFPs against the funder's own filings or site.