FDIC BankFind MCP Server

Compare Bank Snapshot Trends

fdic_compare_bank_snapshots

Read-onlyIdempotent

Compare FDIC reporting snapshots across a set of institutions and rank the results by growth, profitability, or efficiency changes.

This tool is designed for heavier analytical prompts that would otherwise require many separate MCP calls. It batches institution roster lookup, financial snapshots, optional office-count snapshots, and can also fetch a quarterly time series inside the server.

Good uses:

Identify North Carolina banks with the strongest asset growth from 2021 to 2025
Compare whether deposit growth came with branch expansion or profitability improvement
Rank a specific cert list by ROA, ROE, asset-per-office, or deposit-to-asset changes
Pull a quarterly trend series and highlight inflection points, streaks, and structural shifts

Inputs:

state or certs: choose a geographic roster or provide a direct comparison set
start_repdte, end_repdte: Report Dates (REPDTE) in YYYYMMDD format — must be quarter-end dates (0331, 0630, 0930, 1231)
analysis_mode: snapshot or timeseries
institution_filters: optional extra institution filter when building the roster
active_only: default true
include_demographics: default true, adds office-count comparisons when available
sort_by: ranking field (default: asset_growth). All options: asset_growth, asset_growth_pct, dep_growth, dep_growth_pct, netinc_change, netinc_change_pct, roa_change, roe_change, offices_change, assets_per_office_change, deposits_per_office_change, deposits_to_assets_change
sort_order: ASC or DESC
limit: maximum ranked results to return

Returns concise comparison text plus structured deltas, derived metrics, and insight tags for each institution.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`certs`	No	Optional list of FDIC certificate numbers to compare directly. Max 100.
`limit`	No	Maximum number of ranked comparisons to return.
`state`	No	State name for the institution roster filter. Example: "North Carolina"
`sort_by`	No	Comparison field used to rank institutions. Valid options: asset_growth, asset_growth_pct, dep_growth, dep_growth_pct, netinc_change, netinc_change_pct, roa_change, roe_change, offices_change, assets_per_office_change, deposits_per_office_change, deposits_to_assets_change.	asset_growth
`end_repdte`	No	Ending Report Date (REPDTE) in YYYYMMDD format. Must be a quarter-end date: March 31 (0331), June 30 (0630), September 30 (0930), or December 31 (1231). Must be later than start_repdte. Example: 20251231 for Q4 2025. If omitted, defaults to the most recent quarter-end date with published data (~90-day lag).
`sort_order`	No	Sort direction for the ranked comparisons.	DESC
`active_only`	No	Limit the comparison set to currently active institutions.
`start_repdte`	No	Starting Report Date (REPDTE) in YYYYMMDD format. Must be a quarter-end date: March 31 (0331), June 30 (0630), September 30 (0930), or December 31 (1231). Example: 20210331 for Q1 2021. If omitted, defaults to the same quarter one year before end_repdte.
`analysis_mode`	No	Use snapshot for two-point comparison or timeseries for quarterly trend analysis across the date range.	snapshot
`institution_filters`	No	Additional institution-level filter used when building the comparison set. Example: BKCLASS:N or CITY:"Charlotte"
`include_demographics`	No	Include office-count changes from the demographics dataset when available.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.3/5.0

Behavior4/5

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

The description discloses non-obvious internal behavior: it 'batches institution roster lookup, financial snapshots, optional office-count snapshots, and can also fetch a quarterly time series inside the server.' It also describes the return content (text, deltas, metrics, insight tags). Since annotations already cover read-only/idempotent safety, the description adds meaningful behavioral context without contradiction.

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 well-structured and front-loaded with a clear summary, followed by use cases, input explanations, and return details. It is longer than minimal, but the extra length serves a complex 11-parameter tool and helps orient the agent quickly.

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?

For a tool of this complexity with an output schema and comprehensive annotations, the description covers the main behaviors, input choices, and output summary. Minor gaps remain (e.g., what happens if neither state nor certs is supplied), but overall it gives an agent enough context to use the tool correctly.

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

Parameters4/5

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

Schema description coverage is 100%, so a baseline of 3 applies. The description adds semantic value by explaining that 'state or certs' are alternative ways to define the comparison set, and it clarifies the meaning of analysis_mode and include_demographics beyond the schema's dry descriptions.

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 states the tool's function: 'Compare FDIC reporting snapshots across a set of institutions and rank the results by growth, profitability, or efficiency changes.' This provides a specific verb, resource, and scope, and the 'Good uses' examples further distinguish this tool from siblings like fdic_fetch or fdic_search.

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

Usage Guidelines4/5

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

The description explicitly positions the tool for 'heavier analytical prompts that would otherwise require many separate MCP calls' and lists several 'Good uses.' This gives strong when-to-use guidance, though it does not explicitly name alternative tools or mention when not to use it.

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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TDQS

A3.6/5.0

Disambiguation1/5

Several tools have overlapping purposes, including exact duplicates: fdic_fetch/fetch and fdic_search/search. Analytical tools also overlap (analyze_bank_health, ubpr_analysis, detect_risk_signals), making it hard for an agent to distinguish them.

Naming Consistency2/5

Most tools follow a fdic_verb_noun pattern, but two tools (fetch, search) lack the fdic_ prefix, breaking consistency. The verb style varies (get, search, analyze, compare, detect) but the prefix inconsistency is the main issue.

Tool Count2/5

At 29 tools, the server is over the typical limit and includes redundant pairs that inflate the count. The broad FDIC domain justifies many tools, but the duplicates indicate poor scoping.

Completeness4/5

The server covers all major FDIC data resources: institutions, failures, financials, branches, history, demographics, SOD, and summary, plus analytical tools. No major gaps are evident for its read-only data and analysis purpose.

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