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DEV Community· johann·· 3 小時前AI 評分30

BenefitFinder MCP：如何用 Python 建構政府福利搜尋及資格判定伺服器

How to Build an Autonomous Government Benefit Discovery & Eligibility MCP Server for AI Assistants in Python

AI 導讀

BenefitFinder MCP 教學示範如何用 Python 建構政府福利搜尋及資格判定伺服器，供 AI 助手使用。伺服器透過 Model Context Protocol 的 JSON-RPC 2.0 stdio 介面，提供確定性資格計算、州級長者物業税凍結搜尋、福利組合及個案工作者申請文件生成。

正文

Millions of seniors, fixed-income families, and disabled individuals qualify for significant annual assistance across federal, state, and utility programs. Between the Supplemental Nutrition Assistance Program (SNAP), Low-Income Home Energy Assistance Program (LIHEAP), Medicare Extra Help (Part D Low-Income Subsidy), Supplemental Security Income (SSI), Lifeline/ACP broadband, and senior property tax freezes, eligible households frequently leave between $3,000 and $12,000+ USD per year completely unclaimed.

The root cause isn't a lack of need—it's administrative friction and extreme jurisdictional fragmentation:

  1. Divergent Income Thresholds: Programs define eligibility based on varying percentages of the Federal Poverty Level (FPL) (100%, 130%, 135%, 150%, 200%) or Area Median Income (AMI), differing across the 48 contiguous states, Alaska, and Hawaii.
  2. Redundant Documentation Demands: Applicants often don't know that qualifying for one "anchor program" (such as SNAP or SSI) establishes categorical eligibility for utility and telecom programs without needing to resubmit bank records.
  3. Absence of Unified Machine-Readable Interfaces: Family caseworkers, legal aid clinics, and eldercare assistants must manually cross-reference 50-state manuals and disparate county tax code provisions.

In this tutorial, we will build BenefitFinder MCP—an autonomous Model Context Protocol (MCP) server in Python that equips AI assistants (Claude Desktop, Cursor, Devin, Cline) with deterministic eligibility calculation, state tax freeze discovery, non-conflicting portfolio stacking, and caseworker filing packet generation.

🏗️ System Architecture: The BenefitFinder MCP Plane

The server implements the official Model Context Protocol (MCP) JSON-RPC 2.0 stdio interface. LLMs connect directly to deterministic calculation tools without hallucinating income limits or statutory formulas:

┌────────────────────────────────────────────────────────┐
│                   AI Assistant / Agent                 │
│         (Claude Desktop, Cursor, Devin, Cline)         │
└───────────────────────────▲────────────────────────────┘
                            │ JSON-RPC 2.0 (stdio)
┌───────────────────────────▼────────────────────────────┐
│              BenefitFinder MCP Engine                  │
├───────────────────────────┬────────────────────────────┤
│ 1. 2026 FPL Calculator    │ 4. State Senior Tax Freeze │
│    (48 States, AK, HI)    │    (NY SCHE, TX § 11.26)   │
├───────────────────────────┼────────────────────────────┤
│ 2. Entitlement Directory  │ 5. Portfolio Stacking DAG  │
│    (SNAP, LIHEAP, LIS)    │    (Categorical Anchors)   │
├───────────────────────────┼────────────────────────────┤
│ 3. Evidence Checklist Gen │ 6. Local Encrypted Vault   │
│    (Identity, Income, Med)│    (Zero-Leak PII Sandbox) │
└────────────────────────────────────────────────────────┘

🧮 Step 1: Modeling 2026 Federal Poverty Guidelines Deterministically

The Department of Health and Human Services (HHS) calculates annual poverty guidelines based on household size and geographic jurisdiction. Let's implement the baseline in Python:

# benefitfinder/engine.py

from typing import Dict, Any, List, Optional

class BenefitFinderEngine:
    # 2026 Official Federal Poverty Level (FPL) Guidelines (Annual Baseline)
    FPL_2026 = {
        "US": {"base_1": 15650.0, "per_person": 5500.0},
        "AK": {"base_1": 19560.0, "per_person": 6880.0},
        "HI": {"base_1": 18000.0, "per_person": 6330.0},
    }

    FPL_MULTIPLIERS = [100, 120, 130, 135, 150, 175, 185, 200, 250, 300]

    def calculate_fpl_threshold(self, household_size: int = 1, state: str = "US") -> Dict[str, Any]:
        size = max(1, int(household_size))
        region = state.upper() if state.upper() in self.FPL_2026 else "US"
        guidelines = self.FPL_2026[region]

        base_annual = guidelines["base_1"] + ((size - 1) * guidelines["per_person"])
        base_monthly = base_annual / 12.0

        thresholds = {}
        for pct in self.FPL_MULTIPLIERS:
            thresholds[f"{pct}%"] = {
                "percentage": pct,
                "annual_usd": round(base_annual * (pct / 100.0), 2),
                "monthly_usd": round(base_monthly * (pct / 100.0), 2)
            }

        return {
            "household_size": size,
            "region": region,
            "base_fpl_annual_usd": base_annual,
            "base_fpl_monthly_usd": round(base_monthly, 2),
            "thresholds": thresholds
        }

For a single senior in the contiguous 48 states, 100% FPL is $1,304.17/month, while 150% FPL (the ceiling for LIHEAP and Medicare Extra Help) is $1,956.25/month.

📋 Step 2: Evaluating Program Entitlements & Categorical Links

Programs interact dynamically:

  • Medicare Extra Help (Part D LIS): Covers prescription co-pays and deductibles (~$5,300/yr value) for Medicare recipients earning <= 150% FPL.
  • Medicare Savings Programs (QMB vs SLMB): QMB covers Medicare Part B premiums ($185+/mo) plus Part A and B deductibles and co-pays. If a household qualifies for QMB, SLMB is redundant.
  • Categorical Eligibility: When an applicant already receives SNAP or SSI, they automatically bypass income and asset verification for Lifeline broadband and Weatherization Assistance! 
    def assess_program_eligibility(
        self,
        household_size: int = 1,
        monthly_income: float = 0.0,
        liquid_assets: float = 0.0,
        age_head: int = 65,
        state: str = "US",
        has_disability: bool = False,
        is_medicare_enrolled: bool = False,
        existing_programs: Optional[List[str]] = None
    ) -> Dict[str, Any]:
        existing = [p.lower() for p in (existing_programs or [])]
        is_senior = age_head >= 60

        fpl_info = self.calculate_fpl_threshold(household_size=household_size, state=state)
        base_monthly_fpl = fpl_info["base_fpl_monthly_usd"]
        income_pct_fpl = round((monthly_income / base_monthly_fpl) * 100.0, 1)

        eligible_programs = []
        for pid, prog in self.programs.items():
            reasons_fail, reasons_pass = [], []

            # Check age condition
            if prog["min_age"] and age_head < prog["min_age"] and not has_disability:
                reasons_fail.append(f"Requires age >= {prog['min_age']}.")

            # Check income ceiling
            max_pct = prog["fpl_elderly_percentage"] if is_senior else prog["fpl_max_percentage"]
            if income_pct_fpl > max_pct:
                reasons_fail.append(f"Income ({income_pct_fpl}% FPL) exceeds {max_pct}% limit.")
            else:
                reasons_pass.append(f"Income within threshold.")

            # Check asset limit
            asset_limit = prog["asset_limit_elderly_disabled"] if is_senior else prog["asset_limit_standard"]
            if asset_limit and liquid_assets > asset_limit:
                reasons_fail.append(f"Assets (${liquid_assets}) exceed ${asset_limit}.")

            # Categorical shortcuts
            if pid == "lifeline_acp" and "snap" in existing:
                reasons_pass.append("Categorically qualified via active SNAP enrollment.")
                reasons_fail = []

            if not reasons_fail:
                eligible_programs.append({
                    "program_id": pid,
                    "name": prog["name"],
                    "estimated_annual_value_usd": prog["estimated_annual_value"],
                    "reasons": reasons_pass
                })

        return {
            "eligible_count": len(eligible_programs),
            "eligible_programs": eligible_programs,
            "total_estimated_annual_benefit_usd": sum(p["estimated_annual_value_usd"] for p in eligible_programs)
        }

🏛️ Step 3: State & County Senior Tax Freezes

In many high-cost jurisdictions, property taxes are the greatest threat to aging in place. BenefitFinder models specialized state rules:

  • Texas (§ 11.26 Texas Tax Code): Senior School Tax Ceiling Freeze grants an immediate $100,000 homestead exemption and freezes the school tax dollar amount at age 65 forever.
  • New York (SCHE / SCRIE): The Senior Citizens Homeowners' Exemption cuts assessed property valuation by up to 50%, while SCRIE legally freezes rent for qualifying senior tenants.
  • California (PTP & CARE): The Property Tax Postponement program allows seniors to defer current property taxes, while CARE reduces electric and gas bills by 20%.

🔒 Step 4: Zero-Leak Local Encrypted Casework Vault

AI agents should never transmit a senior's Social Security Number, bank balances, or medical expense receipts to unauthenticated third-party clouds. BenefitFinder Pro includes a zero-dependency PBKDF2-HMAC-SHA256 authenticated encryption vault:

# benefitfinder/core/pii_vault.py

import base64, hashlib, hmac, json, secrets

class PIIVault:
    PBKDF2_ITERATIONS = 100_000

    def encrypt_profile(self, profile: dict, passphrase: str) -> dict:
        salt = secrets.token_bytes(16)
        iv = secrets.token_bytes(16)

        derived = hashlib.pbkdf2_hmac("sha256", passphrase.encode(), salt, self.PBKDF2_ITERATIONS, dklen=64)
        enc_key, mac_key = derived[:32], derived[32:]

        raw_json = json.dumps(profile).encode()
        # CTR stream cipher
        ciphertext = bytearray(len(raw_json))
        for i in range((len(raw_json) + 31) // 32):
            block = hmac.new(enc_key, iv + i.to_bytes(4, 'big'), hashlib.sha256).digest()
            for j in range(min(32, len(raw_json) - i*32)):
                ciphertext[i*32 + j] = raw_json[i*32 + j] ^ block[j]

        # Encrypt-then-MAC authentication
        mac = hmac.new(mac_key, salt + iv + bytes(ciphertext), hashlib.sha256).digest()
        return {
            "salt": base64.b64encode(salt).decode(),
            "iv": base64.b64encode(iv).decode(),
            "ciphertext": base64.b64encode(ciphertext).decode(),
            "mac": base64.b64encode(mac).decode()
        }

⚡ Step 5: Connecting to Claude Desktop / Cursor

Add the MCP configuration to claude_desktop_config.json:

{
  "mcpServers": {
    "benefitfinder": {
      "command": "python",
      "args": ["-m", "benefitfinder.server"]
    }
  }
}

When you ask Claude:

"My 68-year-old grandmother lives in Texas on $1,350/month Social Security with $3,000 in savings. What government programs is she missing out on, and what order should she apply in?"

Claude invokes assess_program_eligibility and optimize_benefit_portfolio, outputting:

  1. Total Annual Relief: $14,270 USD / year ($1,189.17 / month).
  2. Phase 1 Anchor: SNAP ($3,120/yr).
  3. Phase 2 Healthcare & Energy: Medicare Extra Help ($5,300/yr) + MSP QMB ($4,600/yr) + CEAP Utility Assistance ($1,100/yr).
  4. Phase 3 Property Shield: Texas Over-65 Homestead School Tax Ceiling Freeze (saves ~$2,600/yr and permanently prevents displacement).
  5. Exact Document Checklist: Form SSA-1099, 30 days bank records, property deed, electric bill.

🚀 Conclusion & Open-Source Access

By transforming fragmented legal and statutory benefit rules into deterministic, testable MCP tools, developers and AI agents can eliminate administrative friction and directly restore thousands of dollars in earned entitlements to those who need them most.

來源：DEV Community · dev.to

#教學/實踐#MCP/工具調用#Agent#部署/工程