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Hmda

by io.github.pipeworx-io
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Hmda Lender Activity

hmda_lender_activity
Read-onlyIdempotent

What a specific mortgage lender did in a year — applications, originations, denials and dollar volume — from the federal Home Mortgage Disclosure Act register, optionally narrowed to a state or county. Identify the lender by its LEI (the 20-character Legal Entity Identifier HMDA files under); resolve_entity maps a company name to one. Covers every US mortgage application 2018-2025. Reports what lenders DISCLOSED, which is a regulatory filing rather than a market estimate.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "lei": "549300FGXN1K3HLB1R50",
      "year": 2023,
      "county": "11001"
    },
    "name": "hmda_lender_activity"
  }
}

Input Schema

NameRequiredDescriptionDefault
leiYes20-character Legal Entity Identifier, e.g. "549300FGXN1K3HLB1R50".
yearNoFiling year, 2018-2025. Defaults to the most recent published.
stateNoOptional two-letter state, e.g. "CA".
countyNoOptional 5-digit county FIPS, e.g. "06037" for Los Angeles.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, open-world, non-destructive behavior, so the safety burden is lifted. The description adds genuine semantic context beyond that: the data spans 2018-2025 and represents regulatory disclosures rather than market estimates, which matters for interpretation.

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?

Front-loaded with the core output (what the lender did, in which year) followed by sourcing, identification, coverage, and a caveat. Three sentences with little waste, though the parenthetical on LEI character count borders on redundant with the schema.

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 usefully enumerates the return fields (applications, originations, denials, dollar volume), names the temporal coverage, and flags the data's regulatory nature. This is close to sufficient for an agent to invoke and interpret the result, with only exact return shape left 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%, including a worked example and per-field docs for lei, year, state, and county, so the baseline is 3. The description adds only a marginal cross-reference (resolve_entity maps a name to an LEI) over what the schema already provides.

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?

States a specific verb+resource+scope: lender activity (applications, originations, denials, dollar volume) for a given year. It names the data source (HMDA register) and clearly differentiates from siblings like hmda_loan_records and hmda_geography_activity by framing this as per-lender aggregates.

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?

Gives clear usage context: identify the lender by LEI, and if you only have a name, use resolve_entity first. It also notes the optional state/county narrowing. It does not explicitly state when to prefer it over hmda_loans_records or hmda_geography_activity, so it stops short of full when/when-not routing.

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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