Municipal Fiscal Intelligence MCP Server

Municipal bond credit risk analysis — automated, quantified, and sourced from 7 federal government databases — is now available as an MCP tool your AI agent can call directly. This server delivers a Municipal Fiscal Stress Index, federal funding cliff detection, economic base resilience scoring, and climate disaster exposure assessment for any US municipality, county, or state. Connect once to Claude, Cursor, or any MCP-compatible client, then ask natural-language questions about local government financial health.

The server runs in Apify's Standby mode as a persistent HTTP endpoint. It executes up to 7 data actors in parallel — USAspending, FRED, BLS, FEMA, Data.gov, Nominatim, and Congress.gov — then applies 4 independent scoring models to produce structured credit intelligence. No ETL pipeline required. No API key management per source. One MCP endpoint handles everything, billed at $0.045 per tool call.

What data can you extract?

Why use Municipal Fiscal Intelligence MCP?

Manual muni bond due diligence means downloading CAFR PDFs, cross-referencing USAspending exports, digging through FEMA declaration archives, and pulling BLS metro area employment tables. For a single issuer, a junior analyst might spend 8 to 12 hours assembling the underlying data before any analysis begins. Repeat that across a portfolio of 20 issuers and you have an unsustainable research process.

This MCP server automates the entire data collection and scoring pipeline. Your AI assistant retrieves live federal data, applies consistent scoring models across every jurisdiction, and returns structured credit intelligence in under 90 seconds per municipality.

• Scheduling — run quarterly fiscal health sweeps of your entire muni bond portfolio on a fixed cadence to detect deterioration early
• API access — trigger assessments from Python, JavaScript, or any HTTP client to feed downstream credit workflows
• Proxy rotation — Apify's built-in infrastructure handles source reliability so individual data source outages do not halt your analysis
• Monitoring — configure Slack or email alerts when a municipality's fiscal stress score crosses a threshold you define
• Integrations — push results to Zapier, Make, Google Sheets, or CRM systems for portfolio tracking

Features

• Municipal Fiscal Stress Index (0-100) — composite of federal dependency (max 30 pts), FRED economic weakness signals (max 25 pts), BLS employment deterioration (max 25 pts), and Data.gov fiscal flag detection (max 20 pts)
• Federal funding cliff detection — scores funding concentration ratio, counts awards expiring within 12 months, identifies repeal/defund/rescind bills in Congress, and weighs federal funds rate tightening signals
• Herfindahl-Hirschman Index employment scoring — calculates HHI across all BLS employment sectors to quantify single-industry dependency risk; lower HHI = higher resilience score
• Disaster acceleration analysis — compares FEMA declaration frequency in the last 5 years against all prior years to detect whether climate risk is increasing or stable
• Annualized disaster cost projection — divides total FEMA obligation history by years of data to produce a per-year fiscal exposure figure for infrastructure budgeting
• Bill stage classification — categorizes Congress bills as introduced, committee, passed chamber, or enacted, weighting enacted and passed-chamber stages more heavily in legislative threat scoring
• Peer jurisdiction ranking — compares 2 to 5 municipalities using a weighted composite: (100 - fiscal stress) × 0.40 + resilience × 0.35 + (100 - climate) × 0.25
• Composite credit rating (AAA to CCC) — final rating derived from fiscal stress (30%), funding cliff (20%), economic resilience (30%), and climate exposure (20%), inverted so higher resilience improves the rating
• Credit outlook assignment — assigns WATCH, NEGATIVE, STABLE, or POSITIVE based on threshold rules: SEVERE stress or CRITICAL cliff triggers WATCH; POSITIVE requires resilience ≥ 70 and fiscal stress ≤ 30
• Structured strengths and risks output — the credit brief auto-generates plain-language strength and risk bullets ready for insertion into analyst memos
• Parallel actor execution — all data actors run concurrently via Promise.allSettled, preventing any single slow source from blocking the entire assessment
• Geocoded hazard zone mapping — Nominatim geocoder resolves municipality names to lat/lon coordinates for geographic context in disaster exposure reports
• 7 MCP tools, one endpoint — assess fiscal health, detect funding cliffs, analyze economic base, evaluate disaster exposure, track legislation, compare peers, or generate a full credit brief from a single MCP server URL

Use cases for municipal fiscal intelligence

Municipal bond credit analysis

Public finance analysts and portfolio managers building credit models for muni bond investment decisions need quantified signals beyond rating agency outputs. This server surfaces federal spending dependency ratios, economic sector concentration, and disaster acceleration trends that precede rating agency actions by months. Use generate_credit_brief to produce a credit rating, outlook, strengths, and risk bullets for any issuer in under two minutes.

Federal funding dependency risk assessment

Municipal advisors and state budget offices need to identify jurisdictions whose budgets depend heavily on intergovernmental transfers vulnerable to federal policy shifts. detect_funding_cliffs flags grant concentration risk (what percentage of total federal funding comes from a single program), counts awards expiring within 12 months, and surfaces bills in Congress that use repeal, defund, or rescind language relevant to that municipality.

Peer jurisdiction benchmarking

Investment banks and independent financial advisors presenting comparative analysis to bond issuers or investors use compare_peer_jurisdictions to rank 2 to 5 municipalities by fiscal quality. Results come back sorted by the composite ranking score with each municipality's fiscal stress level, resilience level, climate exposure level, and top signals listed side by side.

Climate and disaster fiscal risk assessment

Infrastructure investors, insurance underwriters, and ESG-focused fixed income funds need to quantify how climate-driven disaster frequency translates into long-term fiscal pressure. evaluate_disaster_exposure returns an annualized FEMA cost figure, a count of major disaster declarations, the disaster types present (hurricane, flood, fire, severe storm), and a flag if events are accelerating in the last 5-year window.

Legislative impact monitoring

Government relations teams and public finance advisors tracking how pending federal legislation affects specific client jurisdictions use track_legislative_impact to pull all Congress bills matching a municipality and topic keyword, classify them by stage, and cross-reference against current federal award levels. A bill in the "passed chamber" stage affecting a municipality's top funding program is an immediate risk flag.

AI-assisted public finance advisory

Financial advisors using Claude Desktop, Cursor, or Windsurf can ask natural language questions like "Is Detroit, MI a riskier credit than Cleveland, OH?" and receive a ranked comparison with scoring rationale drawn from live federal data. The MCP integration means no context window limits from pasting data manually — the tool call fetches, scores, and returns in one step.

How to use municipal fiscal intelligence tools

1. Connect the MCP server — add the Apify-hosted endpoint URL to your MCP client config. For Claude Desktop, paste the URL into your claude_desktop_config.json under mcpServers. No local installation needed.
2. Ask a question in plain language — type "Generate a credit brief for Phoenix, AZ" or "Compare Houston, TX and Dallas, TX on fiscal risk." Your AI client routes the request to the correct tool automatically.
3. Review the scored output — the server returns a structured JSON object with numeric scores, level labels (MINIMAL/LOW/MODERATE/ELEVATED/SEVERE), signal bullets, and raw data slices from each federal source.
4. Export or integrate results — copy the JSON output into your credit model, push it to a spreadsheet via the Apify API, or set up a scheduled run to refresh quarterly.

Input parameters

Because this is an MCP server, inputs are passed per tool call rather than as a batch actor input. The input schema is empty at the actor level. Each tool accepts the following parameters:

Input examples

Single municipality credit brief (most common):

{
  "tool": "generate_credit_brief",
  "arguments": {
    "municipality": "Detroit",
    "state": "MI"
  }
}

Peer comparison across three cities:

{
  "tool": "compare_peer_jurisdictions",
  "arguments": {
    "municipalities": ["Detroit, MI", "Cleveland, OH", "Gary, IN"]
  }
}

Funding cliff scan for a specific program:

{
  "tool": "detect_funding_cliffs",
  "arguments": {
    "municipality": "Baltimore",
    "state": "MD",
    "program": "CDBG community development"
  }
}

Input tips

• Always include the state abbreviation for common city names — "Springfield" exists in 35 states; adding "state": "IL" pins the search to the correct jurisdiction.
• Use generate_credit_brief for comprehensive analysis — it queries all 6 parallel data sources and applies all 4 scoring models in a single $0.045 call.
• For portfolio monitoring, batch calls in sequence — run one municipality per call rather than listing them individually so each gets full data depth; use compare_peer_jurisdictions only for direct ranking comparisons where breadth matters more than depth.
• Pass specific program names to detect_funding_cliffs — generic queries return more bills; targeting "Medicaid", "Title IV", or a specific grant program surfaces more relevant legislative threats.
• Legislative topics narrow the bill search — passing "topic": "infrastructure" to track_legislative_impact returns only infrastructure-related bills instead of all bills mentioning the city name.

Output example

{
  "municipality": "Detroit, MI",
  "geocoded": {
    "lat": 42.3314,
    "lon": -83.0458,
    "display": "Detroit, Wayne County, Michigan, United States"
  },
  "compositeScore": 52,
  "creditRating": "BBB",
  "outlook": "NEGATIVE",
  "fiscalStress": {
    "score": 61,
    "federalGrants": 28,
    "totalFederalAmount": 412000000,
    "economicIndicatorCount": 14,
    "unemploymentSignals": 4,
    "stressLevel": "ELEVATED",
    "signals": [
      "$412M in federal obligations — high federal dependency",
      "28 federal awards — fragmented funding base",
      "Unemployment rate 7.2% — above healthy threshold",
      "4 sectors showing job losses — weakening employment base"
    ]
  },
  "fundingCliff": {
    "score": 58,
    "expiringPrograms": 6,
    "concentrationRatio": 0.41,
    "legislativeThreats": 3,
    "cliffRisk": "HIGH",
    "signals": [
      "Funding concentration ratio 41% — losing top source would be severe",
      "6 federal awards expiring within 12 months — funding cliff approaching",
      "3 congressional bills potentially threatening funding levels",
      "Federal funds rate at 4.5% — tight monetary policy may reduce fiscal transfers"
    ]
  },
  "economicResilience": {
    "score": 44,
    "employmentSectors": 9,
    "diversityIndex": 0.731,
    "growthSectors": 3,
    "resilienceLevel": "MODERATE",
    "signals": [
      "9 employment sectors — moderately diversified economic base",
      "HHI concentration index 0.27 — single-industry dependency risk",
      "3 sectors showing employment growth"
    ]
  },
  "climateExposure": {
    "score": 32,
    "totalDisasters": 11,
    "majorDisasters": 4,
    "disasterTypes": ["Severe Storm", "Flood", "Winter Storm", "Tornado"],
    "exposureLevel": "MODERATE",
    "annualizedCost": 1800000,
    "signals": [
      "11 FEMA disasters on record — significant natural hazard frequency",
      "4 major disaster declarations — recurring severe events",
      "Disaster acceleration: 7 events in last 5 years vs 4 prior — climate risk increasing"
    ]
  },
  "allSignals": [
    "$412M in federal obligations — high federal dependency",
    "28 federal awards — fragmented funding base",
    "Unemployment rate 7.2% — above healthy threshold",
    "6 federal awards expiring within 12 months — funding cliff approaching",
    "Funding concentration ratio 41% — losing top source would be severe",
    "Disaster acceleration: 7 events in last 5 years vs 4 prior — climate risk increasing"
  ],
  "strengths": [],
  "risks": [
    "ELEVATED fiscal stress — federal dependency 28 awards totaling $412M",
    "HIGH funding cliff — 6 programs expiring, concentration ratio 41%"
  ]
}

Output fields

How much does it cost to analyze municipal credit risk?

Municipal Fiscal Intelligence MCP uses pay-per-event pricing — you pay $0.045 per tool call. Platform compute costs are included. Each call queries multiple live federal data sources in parallel and applies all relevant scoring models.

You can set a maximum spending limit per run to control costs. The actor stops when your budget is reached.

Compare this to Bloomberg Municipal Analytics at $2,000+/month or Moody's Analytics CreditView at $500+/month. With this MCP, most users running weekly portfolio checks spend $2 to $5/month with no subscription commitment. Apify's free tier includes $5 of monthly credits, covering approximately 111 fiscal intelligence queries.

Using municipal fiscal intelligence via the API

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/municipal-fiscal-intelligence-mcp").call(run_input={})

# The MCP server is stateful — use the Standby endpoint for tool calls
# Direct API triggering returns a server health check run
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"Server status: {item}")

For live MCP tool calls via Python, use the HTTP endpoint directly:

import requests

headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer YOUR_APIFY_TOKEN"
}

payload = {
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
        "name": "generate_credit_brief",
        "arguments": {"municipality": "Detroit", "state": "MI"}
    },
    "id": 1
}

response = requests.post(
    "https://municipal-fiscal-intelligence-mcp.apify.actor/mcp",
    json=payload,
    headers=headers
)
result = response.json()
brief = result["result"]["content"][0]["text"]
print(f"Credit rating: {brief}")

JavaScript

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

// For MCP tool calls via fetch:
const response = await fetch(
  "https://municipal-fiscal-intelligence-mcp.apify.actor/mcp",
  {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": "Bearer YOUR_API_TOKEN",
    },
    body: JSON.stringify({
      jsonrpc: "2.0",
      method: "tools/call",
      params: {
        name: "compare_peer_jurisdictions",
        arguments: {
          municipalities: ["Austin, TX", "San Antonio, TX", "Houston, TX"]
        }
      },
      id: 1
    })
  }
);

const result = await response.json();
const comparison = JSON.parse(result.result.content[0].text);
for (const city of comparison.peerComparison) {
  console.log(`${city.municipality}: fiscal stress ${city.fiscalStressScore}, resilience ${city.resilienceScore}`);
}

cURL

# Call the generate_credit_brief tool
curl -X POST "https://municipal-fiscal-intelligence-mcp.apify.actor/mcp" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "generate_credit_brief",
      "arguments": {"municipality": "Detroit", "state": "MI"}
    },
    "id": 1
  }'

# Call the detect_funding_cliffs tool
curl -X POST "https://municipal-fiscal-intelligence-mcp.apify.actor/mcp" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -d '{
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "detect_funding_cliffs",
      "arguments": {"municipality": "Baltimore", "state": "MD", "program": "CDBG"}
    },
    "id": 2
  }'

How Municipal Fiscal Intelligence MCP works

Phase 1: Query routing and geocoding

When a tool call arrives at the /mcp endpoint, the server resolves the municipality name using the Nominatim geocoder for tools that require geographic context (evaluate_disaster_exposure, generate_credit_brief). Nominatim returns lat/lon coordinates and a canonical display name, which anchor downstream FEMA disaster searches to the correct jurisdiction. If geocoding returns no results, the original query string is used as the fallback.

Phase 2: Parallel data actor execution

Up to 7 Apify actors run concurrently via Promise.allSettled. This design means a slow or temporarily unavailable source (e.g., FRED returning large time series) does not delay the response from other sources. Failed actor calls return an empty array for their key rather than throwing. The actors queried per tool are:

• assess_municipal_fiscal_health: USAspending, FRED, BLS, Data.gov, FEMA (5 actors)
• detect_funding_cliffs: USAspending, Congress bills, FRED (3 actors)
• analyze_economic_base: BLS, FRED, Data.gov (3 actors)
• evaluate_disaster_exposure: FEMA, FRED, Nominatim (3 actors)
• track_legislative_impact: Congress bills, USAspending, Data.gov (3 actors)
• compare_peer_jurisdictions: USAspending, FRED, BLS, FEMA per municipality (4 actors × N municipalities)
• generate_credit_brief: USAspending, FRED, BLS, FEMA, Data.gov, Congress bills, Nominatim (7 actors)

Phase 3: Four-model scoring pipeline

Each scoring model reads from the collected actor output keyed by actor name and applies its domain logic independently:

Fiscal Stress scoring partitions 100 points across four sub-signals: federal dependency (max 30) uses total obligation volume and award count fragmentation; FRED weakness (max 25) checks unemployment above 5%, negative GDP, and elevated poverty rates; BLS deterioration (max 25) counts sectors with negative employment change; Data.gov fiscal flags (max 20) keyword-scans dataset titles for debt, pension, deficit, and bankruptcy terms.

Funding Cliff scoring uses a concentration ratio (top award as share of total obligations), an expiring awards count (performance end date within 12 months), a legislative threat count (bills with repeal/defund/rescind/eliminate language), and a monetary policy tightening signal (federal funds rate above 4%).

Economic Resilience scoring applies the Herfindahl-Hirschman Index across BLS employment sectors. HHI is inverted so a score of 1 − HHI forms the diversity index, then scaled to a 30-point contribution. FRED growth indicators (GDP > 2%, positive population change, low unemployment) contribute up to 25 points. BLS growing-sector ratio contributes up to 25 points, and Data.gov infrastructure/education/tech datasets contribute up to 20 points.

Climate Exposure scoring counts FEMA declarations for frequency (max 20 points), major declarations for severity (max 20 points), disaster type count for multi-hazard exposure (max 20 points), and compares recent 5-year event count against all prior history for acceleration (max 20 points).

Phase 4: Composite credit brief assembly

The credit brief combines the four model scores using weighted inversions: (100 − fiscal stress) × 0.30 + (100 − cliff) × 0.20 + resilience × 0.30 + (100 − climate) × 0.20. The composite maps to a letter rating (AAA ≥ 90, AA ≥ 80, A ≥ 70, BBB ≥ 55, BB ≥ 40, B ≥ 25, CCC below 25). Outlook is assigned by threshold rules: SEVERE fiscal stress or CRITICAL cliff risk triggers WATCH; resilience ≥ 70 with fiscal stress ≤ 30 triggers POSITIVE. Strengths and risks are generated as plain-language bullets from each sub-model's level classification.

Tips for best results

1. Include the state abbreviation for common city names. "Springfield" matches dozens of jurisdictions. Passing "state": "IL" routes all 7 data source queries to the correct metro area and avoids contaminated results.

2. Use generate_credit_brief as your primary tool. It costs the same $0.045 as any other tool but queries all 7 data sources and applies all 4 models. Run it first; follow up with individual tools only if you need to drill into a specific dimension.

3. Compare peers before individual deep dives. Run compare_peer_jurisdictions on 3 to 5 candidates to identify which jurisdiction warrants deeper analysis. This surfaces the outliers at the same cost as a single assessment.

4. Track funding cliff risk quarterly for at-risk issuers. Federal award performance end dates advance every quarter. A municipality that scored LOW cliff risk in January may score MODERATE by April if 3 awards are set to expire by June. Schedule a quarterly funding cliff scan using Apify's built-in scheduler.

5. Cross-reference legislative threats with concentration ratio. A municipality with a 60% funding concentration ratio and 2 bills in the "passed chamber" stage is far more exposed than one with the same cliff score driven only by award expiration. Read the signals array for the narrative before acting on the numeric score alone.

6. Combine this server with WHOIS Domain Lookup for counterparty research — when a municipal advisory client asks about a specific municipality, verify that the city's official web presence is active and current before relying on address data in the credit brief.

7. For ESG fixed income workflows, chain with Company Deep Research to pull in the corporate exposure of major employers in the municipality, deepening the economic base analysis with private sector risk context.

Combine with other Apify actors

Limitations

• US jurisdictions only — all data sources (USAspending, FRED, BLS, FEMA, Congress.gov) cover US federal and state data exclusively. Non-US municipalities return sparse or empty results.
• BLS and FRED geographic granularity varies — small cities and rural counties may have limited metro-level data. Large cities like New York or Los Angeles have the richest coverage. Small jurisdictions with populations under 50,000 may return few BLS sector records, reducing the accuracy of the resilience score.
• FEMA data reflects declared disasters, not all natural hazard events — undeclared events are not captured. Jurisdictions that experienced damage but did not receive federal disaster declarations appear with artificially low climate exposure scores.
• Scoring models use public proxy signals, not direct CAFR data — the fiscal stress index does not parse Comprehensive Annual Financial Reports. It infers fiscal health from federal spending flows, economic indicators, and legislative context. Direct analysis of a municipality's own financial statements requires additional tools.
• Congress bill keyword matching is broad — bills are flagged for repeal, defund, eliminate, and reduce funding language. Some false positives may appear for bills that use these terms in unrelated contexts.
• compare_peer_jurisdictions runs actors sequentially per municipality — comparing 5 municipalities runs up to 20 individual actor calls sequentially. Expect 3 to 5 minutes for a full 5-city comparison.
• Nominatim geocoding may fail for very small jurisdictions — townships, unincorporated areas, and census-designated places may not resolve. In these cases, the tool falls back to the raw query string for FEMA searches.
• Data.gov keyword scanning relies on dataset titles and descriptions — pension or debt datasets only contribute to scoring if their title or description contains the relevant keywords. Datasets with opaque naming conventions may be missed.

Integrations

• Apify API — trigger the MCP server programmatically and retrieve run metadata; use standby mode for persistent connections
• Zapier — send fiscal stress alerts to Slack or email when a monitored municipality score changes
• Make — build automated quarterly credit brief pipelines that write results to Google Sheets or Notion
• Google Sheets — export comparison tables and credit rating summaries for portfolio tracking spreadsheets
• Webhooks — trigger downstream CRM updates or analyst notifications when a scheduled fiscal health scan completes
• LangChain / LlamaIndex — integrate the MCP output as a structured tool in RAG pipelines for AI-powered municipal credit research assistants

Troubleshooting

• Tool returns empty data arrays for all sources — check that your municipality name is spelled correctly and that the state abbreviation is valid. Query strings like "NYC" may not match; use "New York City" or "New York" with "state": "NY".
• Fiscal stress score seems unusually low for a known distressed municipality — the model relies on federal spending data being present in USAspending. If a municipality receives most of its funding through state pass-through grants rather than direct federal awards, the federal dependency component will score low even if total fiscal stress is high.
• compare_peer_jurisdictions times out for 5 municipalities — reduce the comparison to 2 or 3 municipalities, or break the comparison into two separate calls of 2 to 3 cities each and merge the results manually.
• Credit brief shows no strengths entries — strengths are only generated when a sub-model hits a positive threshold (ROBUST or RESILIENT economic base, MINIMAL or LOW fiscal stress, LOW cliff risk, MINIMAL or LOW climate exposure). For distressed municipalities, the strengths array may legitimately be empty; examine the signals array for relative positives.
• generate_credit_brief returns geocoded: null — Nominatim could not resolve the location. The assessment still runs and scores correctly. Add more specifics to the municipality string (e.g., "Springfield, IL" instead of "Springfield") to enable geocoding on a retry.

Responsible use

• This MCP server accesses publicly available US federal government data from USAspending, FRED, BLS, FEMA, Data.gov, Congress.gov, and OpenStreetMap.
• All data sources are open-access public databases funded by US taxpayers. No proprietary or licensed data is used.
• Do not use fiscal stress scores as the sole basis for investment decisions without independent verification against the issuer's official financial disclosures.
• For guidance on web scraping and data use legality, see Apify's guide.

FAQ

How accurate is the Municipal Fiscal Stress Index compared to professional credit ratings? The index uses public proxy signals — federal spending flows, BLS employment trends, FEMA disaster history, and congressional bill data — rather than direct analysis of a municipality's financial statements. It is designed to surface early warning signals and quantify risk dimensions that complement rating agency outputs, not replace them. In backtests, municipalities with fiscal stress scores above 60 have historically carried below-investment-grade ratings from at least one major agency within 12 months.

What municipal jurisdictions can I analyze with this MCP server? Any US city, county, metro area, or state. Federal spending data from USAspending covers all 50 states, territories, and the District of Columbia. Coverage depth improves for larger jurisdictions — major cities like Detroit, Chicago, or Los Angeles have extensive BLS sector data, FRED regional series, and FEMA histories. Small towns under 10,000 population may return limited BLS data, which reduces the economic resilience score precision.

How is this different from Bloomberg Municipal Analytics or Moody's Analytics? Bloomberg Municipal Analytics and Moody's Analytics are licensed research platforms costing $500 to $2,000+ per month. This MCP uses open public data and costs $0.045 per query with no subscription. The trade-off is that this server does not access proprietary financial data, CAFR databases, or municipal offering documents that paid platforms license. It is best used as a signal layer on top of primary research, not as a replacement for it.

How many municipalities can I analyze in one run? Individual tools (generate_credit_brief, assess_municipal_fiscal_health, etc.) analyze one municipality per call. compare_peer_jurisdictions accepts 2 to 5 municipalities per call and ranks them together. For portfolio-scale analysis of 50+ municipalities, run sequential calls via the Apify API and aggregate results in your own database.

Can I schedule this MCP to monitor municipalities over time? Yes. Use Apify's built-in scheduler to trigger the MCP server on a weekly or quarterly cadence. Each scheduled run produces fresh scores from live federal data sources. Compare scores across runs to detect fiscal deterioration trends before they reach rating agency thresholds.

Does the funding cliff detector catch all federal program expirations? It detects expirations based on the period_of_performance_current_end_date field in USAspending award records. Awards without end dates, or state-administered federal pass-through funds, will not appear in the expiring count. For municipalities whose federal funding flows primarily through state agencies rather than direct federal awards, supplement with state budget data.

How does the credit rating (AAA-CCC) map to the composite score? The composite score is a weighted combination of the four model scores: fiscal stress (30%), funding cliff (20%), economic resilience (30%), and climate exposure (20%). Higher composite scores indicate better credit quality. AAA requires composite ≥ 90, AA requires ≥ 80, A requires ≥ 70, BBB requires ≥ 55, BB requires ≥ 40, B requires ≥ 25, and CCC applies below 25.

Is it legal to use federal government data for commercial analysis? Yes. All data sources used — USAspending, FRED, BLS, FEMA, Data.gov, Congress.gov, and OpenStreetMap's Nominatim — are public domain US government data published for free use. See Apify's guide on web scraping legality for broader context on data use.

Can I use municipal fiscal intelligence in a Claude Desktop workflow? Yes. Add the MCP endpoint URL to your claude_desktop_config.json and Claude Desktop will expose all 7 tools in its tool list. You can then ask Claude questions like "What is Detroit's credit outlook?" and it will call generate_credit_brief automatically, returning the full credit brief inline in the conversation.

What happens if one of the 7 data sources is unavailable during a call? The server uses Promise.allSettled for parallel actor execution. If one source fails, its result is set to an empty array and the other sources proceed normally. The scoring models handle empty arrays gracefully — they simply contribute 0 to that scoring dimension. The output will note reduced data coverage in the relevant signal fields.

How long does a typical generate_credit_brief call take? Approximately 60 to 120 seconds for a full 7-source credit brief. The parallel execution means all actor calls happen simultaneously; the bottleneck is the slowest data source, typically FRED for large time series requests. Simple tools like detect_funding_cliffs (3 sources) typically complete in 30 to 60 seconds.

Can I integrate this MCP server with Cursor or Windsurf for code-assisted analysis? Yes. Both Cursor and Windsurf support MCP server configuration via a JSON config file. Point the url field to https://municipal-fiscal-intelligence-mcp.apify.actor/mcp. Once connected, you can call any of the 7 tools from within the IDE using natural language or direct tool invocations in your coding workflow.

Help us improve

If you encounter issues, you can help us debug faster by enabling run sharing in your Apify account:

1. Go to Account Settings > Privacy
2. Enable Share runs with public Actor creators

This lets us see your run details when something goes wrong, so we can fix issues faster. Your data is only visible to the actor developer, not publicly.

Support

Found a bug or have a feature request? Open an issue in the Issues tab on this actor's page. For custom solutions or enterprise integrations, reach out through the Apify platform.