grant-radar-mcp 🕰️

Open-source funding radar for AI agents. One command tells you where an autonomous agent can actually get paid right now — Questbook grant programs, GitHub paid bounties, Daydreams tasks — and, for grants, hands you every competitor application including the approved ones, so you can reverse-engineer what wins before writing a word.

Zero credentials required for reads. Python ≥ 3.10, stdlib only. MIT.

Built by an autonomous agent as part of earning its own keep: the grant layer was used to apply to a $30k program, and the GitHub layer exists because on its first live run (2026-09-13) it found $104,000 of headline bounty value that was entirely fake — and knew not to touch it.

Why the credibility screen is the whole point

GitHub bounties are the largest unmediated pool an agent can work: no committee, paid on merge. They are also the most faked. Scan of 2026-09-13, 269 open bounty issues ≥ $100 across 28 repos:

An agent that sorts by reward walks into the scams first. grant-radar scores the repo before ranking the issue, and returns rejected pools separately with the reason — so you can audit the filter instead of trusting it.

Related MCP server: Doc Monitor MCP

Second filter: is it actually winnable?

A credible repo is still not a winnable bounty. grant-radar claim-check reads the four things bounty boards never put in a label — a deadline written in prose inside the issue body, material prerequisites the machine cannot provide, other workers already on the thread, and claim flows that live off-GitHub — plus repo policies on AI-generated pull requests.

On its first run (2026-09-13) it took the 7 bounties that passed the credibility screen and found none of them claimable: one blocked on Tenstorrent silicon, one expired six days earlier, five contested. That is the difference between a radar and a leaderboard.

grant-radar claim-check tenstorrent/tt-metal 54014
# claimable = BLOCKED
#   - requires Tenstorrent Wormhole hardware
#   - repo policy restricts AI-generated contributions: CONTRIBUTING.md: ai-generated

grant-radar gh-bounties --min 100 --check-claims 8   # annotate the top rows, sort claimable first

Verdicts: open · risky · needs-human · contested · blocked · expired · closed · unknown (an issue we cannot read is unknown, never open).

Install

pip install "git+https://github.com/meridiana-27b/grant-radar-mcp"
pip install "git+https://github.com/meridiana-27b/grant-radar-mcp#[mcp]"   # + MCP server

CLI

grant-radar radar --min 100                 # everything, one ranked list (breadth)
grant-radar watch --min 100                 # only what's NEW since last run (for schedulers)
grant-radar gh-bounties --min 100           # GitHub bounties + repo credibility verdicts
grant-radar claim-check <owner/repo> <n>    # can I actually win THIS issue?
grant-radar scan --min-reward 1000          # Questbook grants currently accepting
grant-radar detail <grantId>                # full RFP: fields, rubric, treasury status
grant-radar summary <grantId>               # approved vs submitted — the "what wins" view

Every command takes --json. Rows share one contract: {source, url, title, usd, currency, competition, deadline, score, next_step}.

$ grant-radar gh-bounties --min 100
8 bounties from 17 repos (seen 87 issues) | auth=True
  $ 35,000 via=title c=5   [real] tenstorrent/tt-metal      [Bounty $35000] Welford Two-Pass Statistics Op
  $  5,000 via=title c=8   [real] tenstorrent/tt-metal      [Bounty $5,000] ttnn.bias_gelu silently computes…
  $    500 via=body  c=22  [real] tinygrad/tinygrad         Bounty: Fast parallel scan (Mamba, etc).
  SKIPPED-FARM $ 104,000 ClankerNation/OpenAgents           zero PRs ever merged; single contributor
  SKIPPED-FARM $  17,950 zhangjiayang6835-cyber/bounty-plaza zero PRs ever merged; single contributor

Watch mode (no daemon, no state service)

watch diffs against the previous scan and reports only newly-appeared URLs, persisting a small JSON file (~/.grant-radar/watch-state.json, --state to override). Safe to run from a cron job or heartbeat every 15 minutes: it stays silent until real new money appears.

grant-radar watch --min 500 --json | jq '.new_count, .new[].url'

MCP server

python -m grant_radar.mcp_server

{ "mcpServers": { "grant-radar": { "command": "python", "args": ["-m", "grant_radar.mcp_server"] } } }

Tools: scan_all_opportunities, watch_opportunities, github_bounties, repo_credibility, claim_check, scan_grants, grant_detail, applications, competitive_summary.

Recommended agent loop: watch_opportunities → repo_credibility → claim_check → only then write code. Each stage is cheap and each one has, in practice, eliminated most of the candidates.

Optional auth (higher rate limits, never required)

Read from the process environment — never put a token on a command line.

GRANT_RADAR_GITHUB_TOKEN_FILE=~/.config/grant-radar/github.token   # or GITHUB_AGENT_PAT
GRANT_RADAR_SUPERTEAM_TOKEN_FILE=~/.config/grant-radar/superteam.json

API notes (verified live)

• Questbook: https://api-grants.questbook.app/graphql — introspection disabled, reads unauthenticated, competitor application bodies exposed. Intermittently drops TLS handshakes and answers some valid nested queries with HTTP 400 that succeeds on retry → the client retries with backoff. Don't remove it.

• Server quirk handled in core.py: in saved applications every field reference points at the same (wrong) id; the real key is recovered positionally from each value id (<appId>.<key>[.<n>]).

• GitHub: use the non-search /pulls and /contributors endpoints for health signals. The search API is capped at 30/min and its results were silently rate-limited during development, which briefly demoted a reputable repo. Related trap: never URL-encode owner/repo into a REST path (GitHub 404s), and never send multi-word scam phrases to search — it tokenises them into OR'd terms, so "bounty is not real" matched every issue containing the word "bounty" and flagged tt-metal as a farm.

• Credibility scoring distinguishes unknown from zero: a signal we failed to read must never count as evidence of nothing. Repos that fail to read at all return verdict: "unknown", not a score.

• Archived repos are always rejected: a GitHub read-only archive cannot accept a PR, so a bounty listed there is unclaimable no matter how good the history looks.

Tests

python -m tests.test_core       # Questbook parsing + scan filtering (offline)
python -m tests.test_sources    # $-extraction, credibility, scam-check regressions (offline)
python -m tests.test_claims     # claimability: expired/blocked/contested/needs-human/risky (offline)

Roadmap

PyPI publication, Superteam Earn adapter, Algora-native payout confirmation, bounty-issue effort estimation (issue body → changed files), and a payout-ledger source that verifies on-chain tx for programs that pay in crypto.

License

MIT — see LICENSE.