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Open Country: USDA Section 538 and 515 data

Section 538 loans in a state

list_538_loans
Read-onlyIdempotent

USDA's public Section 538 guaranteed loans in one state (file as of October 19, 2026): count, total amount, apartments, tax credit deals, median loan-to-cost and lenders, then loan rows (project, city, lender, closing date, amount, loan-to-cost, units, rents) newest first, paged and filterable by lender or fiscal year.

Input Schema

NameRequiredDescriptionDefault
limitNoHow many rows to return (1 to 50, default 10).
stateYesTwo-letter state code, e.g. CA.
lenderNoOnly loans whose lender name contains this text, e.g. "Bonneville".
offsetNoRows to skip, for paging (default 0).
min_fiscal_yearNoOnly loans obligated in this USDA fiscal year or later.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already establish readOnly, idempotent, non-destructive, closed-world behavior, so the bar is lower, and the description still adds real value: the data vintage ('file as of October 19, 2026'), the result ordering ('newest first'), and the composition of the response. It does not state pagination ceilings or rate limits, but the essentials for a safe read are covered.

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?

A single dense sentence that front-loads the resource and scope before describing the return payload. It is information-rich with no filler, though the long comma-separated field lists make it slightly heavy to parse.

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?

With no output schema, the description carries the burden of explaining returns and does so by naming the summary metrics and row fields. Combined with the full schema coverage on inputs, an agent has enough to call this correctly; only ordering-vs-offset interaction and hard page caps remain implicit.

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 all five parameters are already documented in the schema with examples and bounds. The description only restates the filterable dimensions (lender, fiscal year) and paging, adding no format or edge-case detail beyond what the schema provides. Baseline 3 applies.

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?

Presents a specific verb and resource — listing USDA Section 538 guaranteed loans for a single state — and enumerates both the summary aggregates (count, total amount, apartments, tax credit deals, median loan-to-cost, lenders) and the loan-row fields it returns. This clearly separates it from siblings like get_538_lender_table or size_538_loan, which serve different lookups.

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

Usage Guidelines3/5

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

The phrase 'paged and filterable by lender or fiscal year' implies the usage pattern, and the 'in one state' scoping tells the agent the required granularity. However, there is no explicit when-to-use or when-not-to-use guidance, and no sibling tool is named as an alternative when a lender table or loan sizing is what the agent actually needs.

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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