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DealMatcher

by com.bonissystems
OverviewDiscussionsAdmin

Conforming and FHA loan limits for a county

loan_limit
Read-onlyIdempotent

The 2026 conforming loan limit for a U.S. county (1 to 4 units) from the Federal Housing Finance Agency's county file, whether the county is above the national baseline (high-balance), the county's FHA loan limits (1 to 4 units) from HUD's county file with its year, and the DealMatcher page to cite. Give a 5-digit county FIPS code, or a county name with its two-letter state.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "loan_limit"
  }
}

Input Schema

NameRequiredDescriptionDefault
stateNotwo-letter state code, needed with county
countyNocounty name, e.g. "Bexar" or "Los Angeles County"; give state too
county_fipsNo5-digit county FIPS code, e.g. "48029" for Bexar County, TX

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-open-world safety, so the bar is lower. The description adds real context beyond them: data provenance (FHFA county file, HUD county file), currency (2026, FHA year reported), and the requirement to cite the DealMatcher page. It does not say what happens for unknown/ambiguous counties.

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 conforming limit and then the FHA limits and citation, followed by a short invocation rule. Every clause carries content, though the run-on structure makes it slower to scan than it could be.

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 return-value burden and does so by enumerating the returned fields (conforming limit 1-4 units, high-balance status, FHA limits with year, citation page). Missing only edge behavior for invalid or non-county inputs.

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?

Schema coverage is 100%, so baseline is 3, but the description adds the mutually exclusive alternative input modes (FIPS alone vs. county name plus two-letter state), which the schema never states. That resolves the main ambiguity for a tool with zero required parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific resource with scope: 2026 conforming loan limit per county (1-4 units), the high-balance flag, FHA limits with year, and a citation page. An agent knows exactly what data comes back. It does not explicitly distinguish itself from siblings like llpa_cost or government_loan_fees, so it stops short of a 5.

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?

Gives input guidance ("Give a 5-digit county FIPS code, or a county name with its two-letter state") which is practical for invocation, but offers no when-to-use/when-not guidance or routing to alternatives such as government_loan_fees. Usage context is implied by the data described rather than stated.

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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