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. Dates show when Glama detected each change.
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 declare readOnlyHint and idempotentHint. Description adds context on return value (state abbreviations and names) without contradicting annotations. Good additional detail.
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?
Two concise sentences with no redundancy. Front-loaded with main purpose, followed by usage guidance. Every word is useful.
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?
Given the tool's simplicity (single parameter, output schema present), the description fully covers what an agent needs: purpose, return type, and usage context. No gaps.
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 coverage is 100% and describes the only parameter (_apiKey) as 'HUD API token'. Description does not add new semantic details beyond the schema, so baseline score of 3 is appropriate.
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?
The description clearly states the tool lists U.S. state codes and names, specifies return value (abbreviations and full names), and distinguishes it as a support tool for other HUD tools.
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?
Explicitly advises using the tool to validate or discover state codes for other HUD tools, providing clear context. Lacks explicit when-not-to-use or alternatives, but the narrow scope makes it sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.