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iit-mcp-hudu

Get Asset Layout

hudu_get_asset_layout
Read-only

Resolve a Hudu asset layout by ID, exact name, or slug; returns a compact summary, or the full layout when expand is enabled.

Instructions

Resolve an asset layout from an id, name or slug. Use when the identifier is a layout name or slug; asset_layouts.get is cheaper for a known id. Returns one record: a compact AssetLayoutSummary (icon_color, sidebar_folder_id, include_passwords, … dropped); expand: true returns the full AssetLayout. Bounded: limit default 25, max 100; the client scan stops at 500 records / 4 pages (RESOLUTION_TRUNCATED, never a partial answer). Not for text discovery: call hudu_search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
optsNo
identifierYesId, exact name/slug, or an identifier object.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
foundYes
recordYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.3

TDQS

A4.5/5.0
Behavior5/5

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

With annotations already covering safety (readOnlyHint) and open-world scope, the description still adds substantial behavioral context: compact vs. full return shape, expand semantics, and precise bounding behavior (limit default 25/max 100, scan capped at 500 records/4 pages, RESOLUTION_TRUNCATED rather than a partial answer).

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?

Front-loaded with purpose then usage then return/bounds/alternatives, and every clause carries information. It is dense and semicolon-heavy, running several concerns into one compound sentence, which slightly reduces readability.

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?

For a read tool with an output schema and safety annotations, the definition covers everything an agent needs: resolution semantics, return shape, a documented truncation boundary, and routing to hudu_search. Nothing material is missing.

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 50%, but the description compensates by explaining the identifier's role (resolution from name/slug vs. id) and what opts.expand true/false yields. It enriches the sparse schema (opts carries no description) without fully documenting every identifier-object key (domain, external_id) beyond what the schema shows.

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?

States a specific verb+resource ("Resolve an asset layout") and enumerates accepted identifier forms (id, name, slug). It distinguishes itself from hudu_search ("not for text discovery") and from a cheaper direct-id path, though the latter ("asset_layouts.get") is an operation name rather than a listed sibling tool, slightly muddying differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use ("when the identifier is a layout name or slug") and when-not ("asset_layouts.get is cheaper for a known id"; "Not for text discovery: call hudu_search"). Both an alternative and the selecting condition are named, leaving nothing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.