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Hud

by io.github.pipeworx-io

Hud Fair Market Rents

hud_fair_market_rents
Read-onlyIdempotent

Get HUD Fair Market Rent ceilings by bedroom count (0–4+) for a county or metropolitan area and year. Pass area_name with a state code for ordinary names such as "Travis County"; the tool resolves HUD's required entity ID. A 5-digit county FIPS is also accepted and expanded to HUD's 10-digit county ID. Omit year for HUD's latest available fiscal year.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoFiscal year (e.g., 2024). Omit for the most recent year.
_apiKeyYesHUD API token
area_nameNoCounty or metro name to resolve (e.g., "Travis County" or "Austin-Round Rock"). Use instead of entity_id.
entity_idNoHUD entity ID, 5-digit county FIPS, or HUD metro code. Omit to resolve area_name or get all areas in the state.
state_codeYesTwo-letter state code (e.g., "CA", "NY", "TX"). Required to get state-level summary.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesFair Market Rent data from HUD API
yearYesFiscal year requested or 'latest'
stateYesTwo-letter state code provided in request
entity_idYesFIPS or ZIP code if provided, null otherwise

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "_apiKey": "your-hud-api-key",
-    "state_code": "CA"
-  },
-  {
-    "_apiKey": "your-hud-api-key",
-    "entity_id": "36061",
-    "state_code": "NY",
-    "year": 2024
-  }
-]New value: +[
+  {
+    "_apiKey": "your-hud-api-key",
+    "state_code": "CA"
+  },
+  {
+    "_apiKey": "your-hud-api-key",
+    "area_name": "Travis County",
+    "state_code": "TX"
+  }
+]
    • addedInput schema / properties / area_name
      Added value: +{
+  "description": "County or metro name to resolve (e.g., \"Travis County\" or \"Austin-Round Rock\"). Use instead of entity_id.",
+  "type": "string"
+}
    • changedInput schema / properties / entity_id / description
      Previous value: -"FIPS code or ZIP code to get FMR for a specific area. Omit to get all areas in the state."New value: +"HUD entity ID, 5-digit county FIPS, or HUD metro code. Omit to resolve area_name or get all areas in the state."
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "data": {
+      "description": "Fair Market Rent data from HUD API",
+      "type": "object"
+    },
+    "entity_id": {
+      "description": "FIPS or ZIP code if provided, null otherwise",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "state": {
+      "description": "Two-letter state code provided in request",
+      "type": "string"
+    },
+    "year": {
+      "description": "Fiscal year requested or 'latest'",
+      "type": [
+        "number",
+        "string"
+      ]
+    }
+  },
+  "required": [
+    "state",
+    "entity_id",
+    "year",
+    "data"
+  ],
+  "type": "object"
+}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-hud-api-key",
+    "state_code": "CA"
+  },
+  {
+    "_apiKey": "your-hud-api-key",
+    "entity_id": "36061",
+    "state_code": "NY",
+    "year": 2024
+  }
+]
  4. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral details: the tool resolves HUD's required entity ID from area_name and expands 5-digit FIPS to a 10-digit county ID, plus the default for omitted year. This enriches the annotation-provided safety profile without contradicting it.

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 three sentences, with the main action front-loaded. Every sentence earns its place: the core action, the entity resolution behavior, and the year default. No waste.

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?

Given the tool has 5 parameters, an output schema, and strong annotations, the description covers all meaningful usage variations: area_name+state_code, FIPS expansion, entity_id omission, and year default. It does not need to explain return values because the output schema exists, and the safety profile is supplied by annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though the schema covers all parameters (100%), the description adds significant meaning beyond the schema by explaining how parameters interact: area_name requires a state_code, FIPS is accepted and expanded, and omitting entity_id/resolves to all areas in the state. This is exactly the kind of parameter semantics that helps an agent invoke the tool correctly.

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 opens with a specific verb and resource: 'Get HUD Fair Market Rent ceilings by bedroom count (0–4+) for a county or metropolitan area and year.' This clearly distinguishes it from sibling HUD tools like hud_income_limits and hud_chas, which address different data sets.

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?

The description provides concrete usage guidance: pass area_name with a state code, use a 5-digit FIPS, or omit entity_id to get all areas in a state; omit year to get the latest fiscal year. However, it does not explicitly mention when to use this tool over alternatives or include exclusion criteria, so it stops short of a perfect score.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes, but several query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, compare_entities, validate_claim) overlap in functionality, which could confuse an agent. The Polymarket and HUD subgroups are well-separated.

Naming Consistency3/5

Tool names use multiple styles: verb_noun (ask_pipeworx), prefixed groups (hud_*, polymarket_*, pipeworx_*), and standalone verbs (forget, recall). While subgroups are consistent, the overall set lacks a uniform pattern.

Tool Count3/5

With 35 tools, the server offers broad data and analytics capabilities. The count is on the high side but justified by the range of features (HUD, general queries, prediction markets, memory, subscriptions). Some tools are highly specialized.

Completeness4/5

The tool surface covers housing data, multi-source querying, prediction markets, memory, subscriptions, and meta-tools. Minor gaps exist (e.g., deeper user account management), but core workflows are well-supported.

Resources