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fast_award_screener_mcp

by lprdgds
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

fast_award_screener_mcp

DK Elite AI Solutions — DIBBS Fast Award Screener

Screens DIBBS RFQs for micro-purchase viability against your Net30 + sub-$1K + 1–2 business day threshold. Zero LLM calls in the pipeline — pure deterministic logic.

Architecture

screen_rfq / screen_rfq_batch
        │
        ▼
┌─────────────────────────────────────┐
│  Node 1: parse_rfq                  │  ← PDF fetch + regex extraction
│  Node 2: check_flags                │  ← QPL/Berry/CMMC/ITAR/CAI/HAZMAT
│  Node 3: score_opportunity          │  ← 0–100 score across 5 dimensions
│  Node 4: lookup_grainger            │  ← Price + margin (skipped if disq'd)
└─────────────────────────────────────┘
        │
        ▼
   HITL Summary → Zapier → Gmail/Slack → You approve → DIBBS Fast Trak

Related MCP server: GovCon

Tools

Tool

Description

screen_rfq

Full pipeline for one RFQ

screen_rfq_batch

Parallel screen up to 10 RFQs, ranked by score

explain_flags

Reference: what each disqualifying flag means

Installation

cd fast_award_screener_mcp
pip install -r requirements.txt
python -m py_compile server.py && echo "✅ Syntax OK"

Claude Desktop Config

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "fast_award_screener": {
      "command": "python",
      "args": ["/path/to/fast_award_screener_mcp/server.py"],
      "env": {}
    }
  }
}

Zapier Integration (HTTP mode)

python server.py --transport streamable_http --port 8001
# Then expose via ngrok:
ngrok http 8001

Zapier webhook → POST to ngrok URL → tool screen_rfq_batch → parse summary → Gmail HITL alert

Scoring Dimensions

Dimension

Max

Notes

Delivery window

30

≤1 day = 30pts, ≤2 day = 22pts

Total value

25

$50–$500 sweet spot

Set-aside match

20

WOSB = 20pts, SB = 14pts

Quantity

15

≤10 units = 15pts

Price clarity

10

Known price = 10pts

Thresholds: BID ≥ 65 | REVIEW 40–64 | SKIP < 40

Files

fast_award_screener_mcp/
├── server.py           ← FastMCP server + 3 tools
├── state.py            ← Single RFQState TypedDict (consolidated)
├── requirements.txt
├── nodes/
│   ├── __init__.py
│   ├── parse_rfq.py        ← Node 1: PDF parsing
│   ├── check_flags.py      ← Node 2: Disqualifying clauses
│   ├── score_opportunity.py ← Node 3: Viability scoring
│   └── lookup_grainger.py  ← Node 4: Grainger price lookup
└── README.md

This server cannot be installed

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license - not found
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quality - not tested
C
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
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