hud_list_statesList all U.S. state codes and names. Returns state abbreviations and full names. Use to validate or discover state codes for other HUD tools.
| Name | Required | Description | Default |
|---|---|---|---|
| _apiKey | Yes | HUD API token |
| Name | Required | Description | Default |
|---|---|---|---|
| states | Yes | List of all U.S. state codes and names |
Changes observed during successful MCP inspections.
Input schema / examplesPrevious value: -[
- {
- "_apiKey": "your-hud-api-key"
- }
-]New value: +[
+ {}
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "states": {
+ "description": "List of all U.S. state codes and names",
+ "type": "object"
+ }
+ },
+ "required": [
+ "states"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "_apiKey": "your-hud-api-key"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey readOnly, openWorld, idempotent, and non-destructive behavior, so the description carries less burden. It adds that the tool returns state names and abbreviations and covers all U.S. states, which is useful but not deeply behavioral. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three short, front-loaded sentences with no wasted words. The main action and output are stated first, and the use case is given in the final sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list/reference tool with one documented parameter, rich annotations, and an output schema, the description is complete. It tells the agent what the tool returns and why it would be used, which is sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with the single required _apiKey parameter already described as 'HUD API token.' The description adds no parameter-specific information, so baseline 3 is appropriate since the schema already handles it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: 'List all U.S. state codes and names.' It also clarifies the output ('state abbreviations and full names') and frames the tool as a reference for other HUD tools, which distinguishes it from sibling HUD data tools like hud_fair_market_rents or hud_income_limits.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear usage context: 'Use to validate or discover state codes for other HUD tools.' It does not explicitly name alternatives or state when not to use it, but the purpose is specific enough that an agent can infer when this reference tool is appropriate.
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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