Hud

Hud Crosswalk

hud_crosswalk

Read-onlyIdempotent

Map ZIP codes to census tracts, counties, CBSAs, and congressional districts. Returns geographic identifiers. Use to translate between location code formats or join datasets.

Input Schema

TableJSON Schema

Name	Required	Description
`type`	Yes	Crosswalk type: 1=ZIP-to-tract, 2=ZIP-to-county, 3=ZIP-to-CBSA, 4=ZIP-to-congressional-district, 7=county-to-ZIP.
`query`	Yes	Input value: ZIP code (for types 1-4), or FIPS county code (for type 7). Example: "90210" or "06037".
`_apiKey`	Yes	HUD API token

Output Schema

TableJSON Schema

Name	Required	Description
`data`	Yes	Geographic crosswalk mapping data from HUD API
`query`	Yes	Input query value provided in request
`crosswalk_type`	Yes	Human-readable crosswalk type label

Schema Changelog

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

Changed1 schema field changedMay 8, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "crosswalk_type": {
+      "description": "Human-readable crosswalk type label",
+      "type": "string"
+    },
+    "data": {
+      "description": "Geographic crosswalk mapping data from HUD API",
+      "type": "object"
+    },
+    "query": {
+      "description": "Input query value provided in request",
+      "type": "string"
+    }
+  },
+  "required": [
+    "crosswalk_type",
+    "query",
+    "data"
+  ],
+  "type": "object"
+}

Changed1 schema field changedMay 6, 2026

addedInput schema / examples

Added value: +[
+  {
+    "_apiKey": "your-hud-api-key",
+    "query": "90210",
+    "type": 1
+  },
+  {
+    "_apiKey": "your-hud-api-key",
+    "query": "06037",
+    "type": 7
+  }
+]

First observedApr 22, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Description adds mapping and translation behavior beyond annotations (readOnlyHint, idempotentHint). It does not contradict annotations. However, it could include more detail on rate limits or authentication scope, though schema covers the API key requirement.

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?

Two sentences, front-loaded with action and output, followed by use cases. Every sentence earns its place with no redundancy. Highly efficient.

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 simple parameters, full schema descriptions, and presence of output schema, the description provides complete context for selecting and using this tool. No missing information.

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 coverage is 100% with clear descriptions for all three parameters. Description does not add per-parameter semantics beyond the schema, but the overall purpose enriches understanding. Baseline 3 is appropriate.

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?

Description clearly states the action ('Map'), resource ('ZIP codes to census tracts, counties, CBSAs, and congressional districts'), and output ('geographic identifiers'). It distinguishes from sibling HUD tools like hud_chas or hud_income_limits which serve different data retrieval purposes.

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?

Explicitly states use cases: 'translate between location code formats or join datasets.' Provides clear context for when to use. Does not explicitly exclude alternatives, but given sibling tools are for different HUD data queries, the guidance is sufficient.

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.

Hud Crosswalk

Input Schema

Output Schema

Schema Changelog

TDQS

Glama MCP Gateway

TDQS

Resources