FDIC BankFind MCP Server

Search Institution Demographics Data

fdic_search_demographics

Read-onlyIdempotent

Use this when the user wants quarterly demographic and market-structure attributes (office counts, metro classification, county/territory codes, geographic reference data) for FDIC-insured institutions. Filter by CERT and/or REPDTE. See fdic://schemas/demographics for the full field catalog.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`cert`	No	Filter by FDIC Certificate Number
`limit`	No	Maximum number of records to return (1-10000, default: 20)
`fields`	No	Comma-separated list of FDIC field names to return. Leave empty to return all fields. Field names are ALL_CAPS (e.g., NAME, CERT, ASSET, DEP, STALP). Example: NAME,CERT,ASSET,DEP,STALP
`offset`	No	Number of records to skip for pagination (default: 0)
`repdte`	No	Filter by Report Date (REPDTE) in YYYYMMDD format (quarter-end: 0331, 0630, 0930, 1231).
`filters`	No	FDIC API filter using ElasticSearch query string syntax. Combine conditions with AND/OR, use quotes for multi-word values, and [min TO max] for ranges (* = unbounded). Common fields: NAME (institution name), STNAME (state name), STALP (two-letter state code), CERT (certificate number), ASSET (total assets in $thousands), ACTIVE (1=active, 0=inactive). Examples: STNAME:"California", ACTIVE:1 AND ASSET:[1000000 TO *], NAME:"Chase"
`sort_by`	No	Field name to sort results by. Example: ASSET, NAME, FAILDATE
`sort_order`	No	Sort direction: ASC (ascending) or DESC (descending)	ASC

Output Schema

TableJSON Schema

Name	Required	Description	Default
`count`	Yes
`total`	Yes
`offset`	Yes
`has_more`	Yes
`truncated`	No
`next_offset`	No
`demographics`	Yes

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds useful context about the nature of the data (quarterly, demographic, market-structure) and points to a schema for the full field catalog, enriching the behavioral picture beyond the annotations.

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

Conciseness5/5

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

The description is just two sentences, front-loaded with the primary use case, followed by key filter guidance and a pointer to the full schema. Every sentence earns its place with no wasted wording.

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?

With a complete schema (100% coverage), an output schema present, and annotations covering safety, the description provides sufficient context for a search tool. It specifies the data type, how to filter, and directs users to the full field catalog, making it complete for its complexity.

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 schema already documents all 8 parameters well. The description adds a minor hint about filtering by CERT and/or REPDTE but does not materially improve parameter understanding. Baseline 3 is appropriate when the schema handles 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 states the tool searches quarterly demographic and market-structure attributes with specific examples (office counts, metro classification, county/territory codes) for FDIC-insured institutions. The verb 'search' plus the resource 'demographics data' distinguishes it from sibling tools that focus on financials, institutions, locations, or history.

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?

Provides explicit guidance on when to use the tool ('Use this when the user wants quarterly demographic and market-structure attributes'). It also suggests key filters (CERT and/or REPDTE), but does not mention alternative tools or when not to use it, which would make it a 5.

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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