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malonestar

gov-data-mcp

by malonestar

fdic-ncua-health-rollup

Retrieve FDIC/NCUA bank and credit union financial health metrics with QoQ deltas, peer percentile scoring, and risk flags.

Instructions

Bank & Credit-Union Financial Health API — FDIC/NCUA QoQ. Bank & credit-union financial-health API on keyless FDIC data (NCUA v1.1): unified per-institution assets, deposits, equity, ROA, ROE, NIM and asset quality, with quarter-over-quarter deltas, peer-percentile scoring, and derived health flags (deposit outflow, low ROA, rising NPL). Reads live from the official government source. Store page: https://apify.com/malonestar/fdic-ncua-health-rollup

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoEvery mode returns the SAME full field set — capital ratios, uninsured deposits, unrealized losses, CRE concentration, credit quality and quarter-over-quarter deltas. Mode changes the ordering only. snapshot = largest institutions first. delta = biggest quarter-over-quarter deposit move first (the headline run-risk signal). score = highest peer asset percentile first. stress = most health flags first, the triage view. Example: "stress". Applied by default if omitted: "snapshot".
stateNoUS state to scope the cohort, e.g. CA, TX, NY. Strongly recommended: it focuses the run and makes peer percentiles state-level. Empty = the entire country (slower; national peer scoring). Example: "TX".
benchmarkNoAdds asset-weighted benchmark ratios and this bank's distance from them, in percentage points: benchmark_uninsured_deposit_ratio, benchmark_cre_to_tier1_pct, benchmark_unrealized_loss_to_equity_pct plus uninsured_vs_benchmark_pts, cre_vs_benchmark_pts, unrealized_vs_benchmark_pts. 'national' compares against all ~4,350 FDIC-insured banks; 'state' against the banks in your state. Costs exactly ONE extra request thanks to server-side aggregation — not a second full download. 'none' skips it. Example: "national".
creGrowthNoThe 2006 interagency CRE guidance is TWO tests: construction >= 100% of capital, OR (CRE >= 300% of capital AND CRE grew >= 50% over 36 months). Leaving this on fetches the quarter from 12 quarters ago — one extra request — and fills cre_growth_36m_pct, cre_total_loans_36m_ago, cre_baseline_date and cre_guidance_prong, plus the cre_guidance_both_prongs flag. Turn it off to skip that request; the level tests still run. Example: true.
maxAssetsNoOnly include institutions with at most this many total assets, in thousands of dollars. 0 = no ceiling. Combine with minAssets to score within an asset-size peer band (e.g. community banks $250M–$1B). Applied by default if omitted: 0.
minAssetsNoOnly include institutions with at least this many total assets, in thousands of dollars (FDIC reports assets in $000s, so 1000000 = $1B). Use with maxAssets to build a peer band. 0 = no floor. Applied by default if omitted: 0.
peerBasisNoWhat counts as a 'peer' when computing peer_asset_percentile, peer_roa_percentile, peer_cre_percentile and peer_uninsured_percentile. 'cohort' scores against everything you pulled (a state cohort mixes a $27M agricultural bank with a $200B trust bank, so the percentile means little). 'business_line' uses the FDIC SPECGRP business-model peer group — the cut a bank examiner uses. 'asset_band' uses FFIEC-style size bands. 'community_bank' splits on the FDIC community-bank research flag. Groups with fewer than 5 institutions fall back to the full cohort rather than ranking a bank against two neighbours. Example: "business_line". Applied by default if omitted: "cohort".
maxResultsNoMaximum number of institution-health records to return after filtering, scoring, and ranking. This is your cost cap: one record = one billable result. 500 covers a full mid-size state; TX has ~380 banks, CA ~180. Example: 500. Applied by default if omitted: 1000.
priorItemsNoOptional. In delta mode, an array of institution rows from a previous run (each needs id, total_assets, total_deposits) to diff the current quarter against, instead of auto-fetching the prior quarter. Lets you compare two arbitrary runs. Applied by default if omitted: [].
institutionTypeNoWhich institutions to include. 'bank' = FDIC-insured banks, fully supported, the only option that returns data today. 'credit_union' = NCUA — NOT AVAILABLE YET (ships in v1.3); selecting it alone fails the run immediately and bills nothing, rather than quietly handing back bank data. 'all' = runs the bank half now and picks up credit unions automatically the moment v1.3 lands. Example: "bank".
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description carries the transparency burden. It discloses keyless access, live reads from the official source, and derived health flags, which is useful behavioral context. But it does not mention operational traits like output size limits, pagination, rate limits, or write/read-only status; the cost per record is buried in a parameter description, not in the main description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, information-dense paragraph plus a store link; no filler or tautology. It front-loads the core purpose and key metrics and closes with a useful source reference. It could be slightly more structured (e.g., bullets) but is appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by enumerating the major return field categories (assets, deposits, equity, ROA, ROE, NIM, asset quality, health flags, deltas) and explaining the mode/benchmark concepts are deferred to schema. It gives enough domain context to understand what the tool returns, though it does not specify output shape or size bounds. For a 10-parameter tool, this is reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and every parameter has extensive meaning (examples, defaults, behavior, tradeoffs). The tool description itself adds no parameter-level detail, instead summarizing the domain. Per the baseline rule, with high schema coverage, a score of 3 is appropriate; the schema is doing the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies a specific resource: a bank and credit-union financial-health API built on FDIC/NCUA data, with explicit mentions of metrics (ROA, ROE, NIM), quarter-over-quarter deltas, peer-percentile scoring, and health flags. This distinguishes it sharply from the environmental/geospatial sibling tools. The verb+resource structure is implicit ('API ... provides') but unambiguous and highly specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states the tool reads live government data and focuses on financial health, which implies when it would be appropriate (bank health analysis) versus the environmental screeners. However, it offers no explicit when-to-use or when-not-to-use guidance, no named alternatives, and no exclusion criteria. Usage context is present but only implied by the domain.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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