FDIC BankFind MCP Server

Deposit Market Share Analysis

fdic_market_share_analysis

Read-onlyIdempotent

Analyze deposit market share and concentration for any MSA or city using FDIC Summary of Deposits data, ranking banks and calculating HHI for DOJ/FTC merger guidelines.

Instructions

Analyze deposit market share and concentration for an MSA or city market using FDIC Summary of Deposits (SOD) data.

Computes market share for all institutions in a geographic market, ranks them by deposits, and calculates the Herfindahl-Hirschman Index (HHI) for market concentration analysis per DOJ/FTC merger guidelines.

Two entry modes:

MSA market: provide msa as the numeric MSABR code (e.g., msa: 19100 for Dallas-Fort Worth-Arlington, msa: 42660 for Seattle-Tacoma-Bellevue). Use fdic_search_sod to look up MSABR codes.
City market: provide city (branch city name, e.g., "Austin") and state (two-letter code, e.g., "TX").

Output includes:

Market overview with total deposits, institution count, and HHI classification
Optional highlighted institution showing rank and share (provide cert)
Top institutions ranked by deposit market share
Structured JSON for programmatic consumption

Requires at least one of: msa (numeric MSABR code), or city + state.

Input Schema

TableJSON Schema

Name	Required	Description
`msa`	No	FDIC MSABR numeric code for the Metropolitan Statistical Area (e.g., 19100 for Dallas-Fort Worth-Arlington, 42660 for Seattle-Tacoma-Bellevue). Use fdic_search_sod with MSABR to look up codes.
`cert`	No	Highlight a specific institution in the results.
`city`	No	City name (e.g., "Austin"). Requires state.
`year`	No	SOD report year (1994-present). Defaults to most recent.
`state`	No	Two-letter state abbreviation (e.g., TX). Required when using city filter.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedv3.0.1Sep 22, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

addedInput schema / properties / cert / maximum
```
Added value: +9007199254740991
```
addedInput schema / properties / msa / maximum
```
Added value: +9007199254740991
```
addedInput schema / properties / year / maximum
```
Added value: +9007199254740991
```

changedOutput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

changedOutput schema / additionalProperties
```
Previous value: -trueNew value: +{}
```

Addedv1.26.0May 1, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already declare read-only, idempotent, non-destructive behavior, so the description only needs to add context. It does so well by disclosing the two entry modes, the output contents (overview, HHI classification, optional highlighted institution, top rankings, structured JSON), and the requirement for at least one valid market identifier.

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 with clear sections for entry modes, output, and requirements. It is slightly longer than strictly necessary because much of the content restates schema descriptions, but the bulleted format and front-loaded purpose keep it readable and scannable.

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

Completeness5/5

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

For a read-only analytical tool with an output schema and complete annotations, the description covers the necessary invocation requirements, data source, market entry modes, and output highlights. An agent has enough information to call the tool correctly without needing return-value details that are already available in the output schema.

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%, so the baseline is 3. The description reinforces parameter relationships (city requires state, msa is a numeric MSABR code, cert highlights an institution) but does not add substantial new meaning beyond the schema. It is adequate but not exceptional.

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 names a specific verb and resource: analyze deposit market share and concentration using FDIC Summary of Deposits data. It clearly differentiates itself from sibling search-focused tools by describing the analytical output (market share, rankings, HHI) rather than just retrieval.

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 provides clear entry-mode guidance (MSA vs. city+state), states the required parameter combinations, and cross-references fdic_search_sod for MSABR code lookup. It does not explicitly contrast with every sibling, but the usage context is sufficiently clear for an agent to choose this tool over plain search tools.

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