Ask out loud what public funding you can actually apply for — and get an answer with real numbers, or none at all.
Every year billions in public grant funding goes unclaimed, not because people
are unqualified but because nobody has time to read grants.gov on a Tuesday
night. Opportunity Radar is a self-hosted MCP server that turns that
database into something you can ask a question to: "any grants open for a
small AI company?", "what closes in the next two weeks?", "am I even
allowed to apply for that one?"
Because Alexa+ speaks MCP, the same server answers in the kitchen.
Built for the Build, Ship, Shape: Amazon Developer Hackathon — Alexa+ track, with the AWS Builder and Open Source mini challenges.
A funding answer that is confidently wrong costs someone a weekend. So the honesty rules are structural, not prompt-deep:
| Failure mode | What this server does |
|---|---|
| Source down | Returns error: source_unavailable + "I have no numbers for you" — never an estimate |
| Notice publishes no applicant list | Reports status: see_notice, not "you're ineligible" |
| Opportunity has no deadline | Counted and reported separately, never silently dropped |
| Model ranks fit | Model may set score and reason only; deadlines and dollar figures are attached from grants.gov afterwards |
The test suite asserts each of those (tests/test_radar.py::HonestyUnderFailure).
| Tool | Question it answers |
|---|---|
find_opportunities |
"What's open for rural health right now?" |
opportunity_detail |
"How much is it, and who runs it?" |
check_eligibility_fit |
"Can an individual apply, or only universities?" |
deadline_watch |
"What do I have to act on this week?" |
weekly_briefing |
The scheduled Monday sweep across all your interests |
rank_for_me |
"Of these, which are actually worth my Saturday?" (Bedrock) |
git clone https://github.com/kevin9327/opportunity-radar && cd opportunity-radar
python -m radar.server # http://0.0.0.0:8080/mcpNo API key is needed: grants.gov search is public. Responses cache for 6h.
# point any MCP client at it
curl -s localhost:8080/mcp -H 'content-type: application/json' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"deadline_watch","arguments":{"interest":"artificial intelligence","within_days":30}}}'rank_for_me asks a model whether each opening is worth your Saturday. It
prefers a local model over Ollama — no key, no account, and the sentence
describing your situation never leaves the house. That matters when the query
is "grants for a family caring for a disabled child".
ollama pull llama3.1:8b # engine: localFallbacks are automatic and always labelled in the response: Amazon Bedrock if
AWS credentials happen to exist, then plain keyword overlap so the radar still
answers offline. Set RADAR_RANK_ENGINE=local|bedrock|overlap to pin one.
flowchart LR
A["Alexa+ / any MCP client"] -->|Streamable HTTP · spec 2025-11-25| B["radar.server<br/>JSON-RPC + SSE"]
B --> C["6 tools<br/>honesty gates"]
C --> D["grants.gov<br/>search2 + fetchOpportunity"]
C -->|fit score + reason only| E["Amazon Bedrock<br/>Claude Haiku"]
C --> F[(6h disk cache)]
radar/server.py implements the transport by hand against the MCP spec — one
endpoint, initialize handing out a session id, tools/list schemas generated
from Python type hints, tools/call returning both spoken text and
structuredContent. No framework in the way, which made the spec easy to read
off the wire while debugging.
python -m unittest discover -s tests -v # 19 offline, 2 live skipped
RADAR_LIVE=1 python -m unittest discover -s tests # all 21, incl. grants.govCI runs the offline suite on every push and the live-source suite weekly, so a silent API change shows up as a red build rather than a wrong answer.
The three problems that cost me the most time are written up in FRICTION_LOG.md — two of them shipped as silent wrong answers before I caught them, and both are now pinned by tests.
MIT © 2026 kevin9327