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mcp-sam-gov

fdic_industry_summary

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Retrieve FDIC annual banking aggregates for the US or a state, split by charter class, with assets, deposits, net income, equity, and institution counts.

Instructions

FDIC banking-sector ANNUAL AGGREGATES — total assets, deposits, net income, equity & net interest income + institution/office/branch/employee counts for the whole US OR one state, split by charter class (keyless; api.fdic.gov/banks/summary). Filters (all optional): year (→YEAR), state (→STALP — NOTE: /summary uses STALP, NOT PSTALP; accepts TX/CA/DC/GU/PR or ROLL-UP codes USA/US/OT/PI), charterClass (CB=commercial, SI=savings; omit for both). limit (≤1000), offset (≤100000), sortBy (YEAR/ASSET/DEP/NETINC/BANKS), sortOrder. Returns { summary:[{ year, charterClass, charterClassCode, geography, stateCode, stateFips, scope, isRollup, institutionCount, officeCount, branchCount, employeeCount, totalAssetsUSD, totalDepositsUSD, netIncomeUSD, totalEquityUSD, netInterestIncomeUSD, id }] }. ★ROLL-UP HONESTY: STALP ∈ {USA,US,OT,PI} are GEOGRAPHIC AGGREGATES (isRollup:true). NEVER sum a roll-up row with jurisdiction rows or across scopes — USA is the one national figure; a roll-up is NOT a state. ★netInterestIncomeUSD is net interest INCOME ($ sum), NOT the margin ratio; NO ratio fields (ROA/ROE); derive from netIncomeUSD/totalAssetsUSD/totalEquityUSD. NO name/city filter — FDIC /summary search is ignored; drill via fdic_search_institutions. HONESTY: totalAvailable is EXACT meta.total; money ($thousands → whole USD ×1000, null-never-0; genuine 0 stays 0; absent → null); counts pass through unscaled; non-int year rejected pre-fetch; ONLY honest empty is meta.total:0/data:[] → complete:true/total:0; any other envelope THROWS. NOTE: FDIC keys on CERT, not SAM UEI/DUNS.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoFilter by aggregate YEAR (→ YEAR filter). 1934..current UTC year. e.g. 2023 → the 121 (charter × geography) aggregate rows for 2023. A non-int is rejected pre-fetch (a malformed year is a live HTTP-200 total:0 false-empty).
limitNoRows per page, 1..1000, default 100.
stateNoFilter by geography via the STALP code (uppercase 2-or-3 letters; → STALP filter — the /summary state field is STALP, NOT PSTALP). Accepts a jurisdiction USPS code (TX, CA, DC, GU, PR…) OR a ROLL-UP code: USA (all states+territories), US (states+DC), OT (all territories), PI (Pacific Islands). The output scope/isRollup disambiguates every returned row.
offsetNo0-based row offset for pagination, 0..100000, default 0.
sortByNoSort field (allowlisted enum; default YEAR = aggregate year). An unknown field is rejected before fetch.
sortOrderNoSort direction, default DESC (newest year / largest first).
charterClassNoFilter by charter class (→ CB_SI filter): CB = commercial banks, SI = savings institutions. Omit to return BOTH charter rows for the geography — there is NO pre-combined 'all institutions' row (a geography's total = its CB row + its SI row).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.12.0

TDQS

A4.8/5.0
Behavior5/5

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

The description is exceptionally transparent about behavior beyond annotations. It discloses roll-up honesty (never sum roll-ups with jurisdiction rows), the exact meaning of netInterestIncomeUSD (income, not margin), money scaling ($thousands to whole USD), null vs zero handling, error conditions (non-int year rejected pre-fetch, only honest empty), and the key on CERT vs SAM. This far exceeds the readOnlyHint/openWorldHint annotations, which are minimal.

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 long but highly structured, with bullet-like formatting (★ and HONESTY sections) that front-loads the core purpose and then provides necessary details. Each sentence adds value, particularly the honesty and edge-case notes. While not as concise as the ideal, the density is justified given the complexity and potential for misuse.

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?

Despite no output schema, the description explicitly lists the exact return structure with all fields. It also covers error handling, scaling, roll-up semantics, and differentiation from related tools. Everything an agent needs to correctly call and interpret the tool is present, including the note about FDIC keying on CERT. The completeness is outstanding for a complex aggregate tool.

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?

While schema description coverage is 100% and the schema already explains parameters, the description adds valuable context by reiterating key constraints (STALP vs PSTALP, roll-up codes, charter class behavior) and introduces the 'ROLL-UP HONESTY' and money-scaling notes that clarify how parameters like state and year affect results. This goes beyond mere repetition, though much is redundant.

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 it provides FDIC banking-sector annual aggregates with specific fields (assets, deposits, net income, etc.) for the whole US or one state, split by charter class. It differentiates from siblings like fdic_search_institutions by explicitly stating there is no name/city filter and directing drill-down there. The verb 'returns' and resource 'FDIC /summary' are specific and unambiguous.

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

Usage Guidelines5/5

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

It explicitly states when to use this tool: for aggregate data, and when not: for name/city search, directing to fdic_search_institutions. It also clarifies filter usage, including the STALP vs PSTALP nuance and charter class handling. The 'HONESTY' sections provide critical usage cautions about roll-ups and data scaling, leaving no ambiguity about proper invocation.

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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