FDIC BankFind Suite MCP for AI. Analyze US banking history from public records.
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FDIC BankFind Suite provides direct access to public records from the Federal Deposit Insurance Corporation (FDIC). Query detailed information on US banks, including institutional demographics, operational branch locations, merger history, and financial performance summaries.
Use your AI agent to run complex banking analysis against verified federal data instantly.
What your AI can do
List demographics
Retrieves general demographic information pertaining to financial institutions.
List failures
Lists specific details regarding failed financial institutions, allowing filtering by failure type or date.
List financials
Retrieves detailed financial reports and information for a selected institution.
Find specific financial institutions using advanced filters for name, certificate number, or state.
List all physical branches and operational addresses tied to a given bank or institution.
Review the timeline of mergers, acquisitions, and other major organizational shifts for any bank.
Access historical aggregate data points, such as Summary of Deposits (SOD) figures, to gauge institutional size over time.
Query specific details on banks that have failed, including the date and resolution type.
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FDIC BankFind Suite: 8 Tools for Banking Data
These eight tools allow you to query every aspect of a US bank's public record—from its current location status to decades-old structural changes.
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Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using FDIC BankFind Suite on VinkiusList Demographics
Retrieves general demographic information pertaining to financial institutions.
List Failures
Lists specific details regarding failed financial institutions, allowing filtering...
List Financials
Retrieves detailed financial reports and information for a selected institution.
List History
Provides a chronological record of structural changes, such as mergers or...
List Institutions
Searches and lists information on financial institutions (banks) using filters like...
List Locations
Lists specific physical locations and branches for a given financial institution, filterable by city or zip code.
List Sod
Retrieves data on the Summary of Deposits (SOD) figures for an institution.
List Summary
Generates a summary view combining historical and aggregate financial data points...
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 8 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Gathering bank details used to take hours of clicking through federal databases.
Right now, checking a single institution's status means jumping between dozens of government portals. You search by name in one place, download location lists from another, and then manually cross-reference merger dates on a third site just to piece together a basic timeline.
With this MCP, your agent does all that heavy lifting for you. Instead of clicking through multiple tabs, you simply ask: 'Show me every structural change and branch location for Bank X.' The answer comes back instantly, structured and ready for use.
The FDIC BankFind Suite provides instant access to critical banking data.
Manually gathering financial context meant tracking down Summary of Deposits (SOD) reports from year to year, then finding the corresponding structural records. This process was tedious and prone to human error because every document required a separate download and comparison.
Now you combine these steps into one prompt. You ask for both the historical record and the deposit totals. The MCP hands you a single, synthesized data set that gives immediate clarity on institutional size and stability.
What your AI can actually do with this
Need a full picture of a bank's standing? This MCP connects you directly to public FDIC records for deep dives into the US banking sector. You can ask your agent to find specific banks by name or certificate number, then map out every branch location associated with that institution. Beyond simple lookups, you can analyze how an organization has changed over decades, tracking everything from mergers and acquisitions to structural shifts.
Need financial context? Pull historical data on deposits or get the latest summary figures. When your AI client accesses this through Vinkius, it turns a massive pile of government documents into natural conversation prompts, giving you actionable insights without having to navigate complex databases yourself.
019e3895-0883-7166-9426-e05cd7d1779d Here's how it actually works
The bottom line is you get complex banking analysis without ever seeing an API endpoint or writing SQL.
Subscribe to this MCP in Vinkius. If you need high-volume queries, provide an FDIC API Key.
Direct your AI client to run a query using the natural language interface.
Your agent processes the request against the public records and returns structured data on the bank's status.
Who is this actually for?
Compliance officers, financial analysts, and investigative journalists. If your job requires knowing exactly where a bank operates, how large it was five years ago, or if it's survived any recent structural changes, this is for you.
Determining an institution’s risk profile by comparing its historical financial summaries with its current asset base.
Verifying the active status, physical branch locations, and operational history of a bank across multiple states for regulatory checks.
Building a timeline of industry consolidation by tracking mergers and acquisitions or reviewing records of failed institutions in specific years.
What Changes When You Connect
You can trace a bank's entire corporate timeline by running list_history, immediately showing major mergers and acquisitions over time. This builds a structural background that simple name searches miss.
Get a full picture of an institution’s size with list_summary. By cross-referencing this aggregate data with the detailed financial reports from list_financials, you build a much stronger risk assessment.
Don't just check if a bank exists. Use list_institutions to find specific records, and then immediately use list_locations to map every branch across different states for compliance checks.
If investigating instability, running list_failures gives you the names of failed banks. You can then cross-reference these results with demographics using list_demographics to see industry trends.
When analyzing deposits, querying list_sod provides a specific snapshot of deposit totals that complements the broader data found in list_financials, giving depth to your quantitative findings.
See it in action
Tracking corporate instability
A journalist needs to know if Bank X has been acquired or merged. They first use list_institutions to find the bank's primary record, then immediately run list_history. This pinpoints every structural change, allowing them to write an accurate timeline of industry consolidation.
Compliance location audit
A compliance officer must verify that all branches in a new county are properly registered. They use list_locations, filtering by the specific ZIP code and state, ensuring no physical operational points were missed before issuing permits.
Assessing financial stability
An analyst wants to see if a bank's revenue decline is a long-term trend. They combine list_sod data for the last five years and run it through list_summary to get an aggregate view of how its overall size has changed.
Post-failure analysis
A researcher wants to understand why Bank Y failed in 2018. They use list_failures to confirm the failure record, then check list_financials for that year's detailed reports to find a potential financial trigger.
The honest tradeoffs
Searching by name alone
Just asking 'What is the status of Chase?' This gives vague results and doesn't tell you about its history or branches.
Always narrow the scope. Start with list_institutions to get the certificate number, then use that ID to run list_locations for all branches.
Missing historical context
Seeing a bank's current balance sheet and assuming it has always been stable. This ignores past mergers or failed periods.
Always check the timeline first. Run list_history to understand any structural changes before trusting the current financial data from list_financials.
Stopping at surface-level data
Only checking if a bank is listed as 'active' without knowing its total branch count or deposit volume.
Layer your queries. Use list_institutions to find the ID, then run both list_locations (for physical presence) and list_sod (for financial scale).
When It Fits, When It Doesn't
Use this MCP if you need a quantitative risk profile based on verifiable, historical US banking records. You need to know who they are (list_institutions), where they operate (list_locations), and how their finances have changed over time (list_summary combined with list_financials). Don't use this if you're trying to predict market trends or assess the impact of a new piece of legislation. For predictive risk, you need external data feeds that track sentiment; this MCP is for historical facts and structural verification only.
Questions you might have
How do I find all branches in Texas using list_locations? +
Filter by state or county when calling list_locations. You just need to provide the geographical criteria, and the MCP returns a comprehensive list of physical locations for that area.
What is the difference between list_financials and list_summary? +
list_financials gives you detailed reports on a specific metric, while list_summary provides an aggregate overview combining multiple historical data points for easier comparison.
How can I find out if a bank has ever merged using list_history? +
Use the institution's certificate number with list_history. The results will show every recorded event, detailing which other entities were involved in mergers or acquisitions.
Can I find all failed banks from a specific year using list_failures? +
Yes. You can filter the tool by the failure date or type (like 'SAVING' or 'FAILYR') to quickly build a report on bank failures for that period.
I need to run many queries using list_institutions; how do I manage rate limits? +
You should secure an FDIC API Key for higher rate limits. Providing this key allows your agent to execute bulk data pulls and maintain stability during large-scale analysis.
How can I perform complex filtering, like finding banks in a specific state with a name pattern, using list_institutions? +
You must use Elastic Search syntax within the filters. This lets you combine criteria (e.g., STATE: TX AND NAME:JPM) to narrow down results beyond simple searches.
What types of financial entities are included when I run list_demographics? +
This MCP provides data on a wide range of US banks and regulated financial institutions. It focuses on publicly reported demographic metrics rather than private lending groups.
If I use list_locations to find multiple branches, can I then pull the specific financials for each one using list_financials? +
Yes, you link them by institution identifier. After retrieving a set of locations, your agent passes the relevant primary certificate number to list_financials to get that branch's data.
Can I search for banks in a specific state? +
Yes. Use the list_institutions tool with a filter like STALP:"NY" to find all institutions headquartered in New York.
How do I find information about bank mergers? +
You can use the list_history tool. It provides details on structure change events, including mergers and acquisitions for financial institutions.
Can I see data on banks that have failed? +
Yes, the list_failures tool allows you to retrieve records of failed financial institutions, including the date of failure and resolution type.
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