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Schema.gov.it MCP Server

Schema.gov.it MCP Server

Official
by italia
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TypeScript
Hybrid

Explore Classes

explore_classes
Read-onlyIdempotent

List ontology classes with instance counts, filterable by URI pattern, ordered by count descending.

Instructions

List available classes in the ontology with instance counts.

Args:

  • limit: Maximum number of classes to return (default: 50)

  • filter: Optional regex filter for class URI (case-insensitive)

Returns:

  • List of classes with instance counts, ordered by count descending

Examples:

  • No args: Returns top 50 classes by instance count

  • filter="Person": Returns classes containing "Person" in URI

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "explore_classes"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
filterNoOptional text filter for class URI

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.7/5.0
Behavior5/5

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

The description fully discloses behavior beyond annotations: results are ordered by count descending, filter is regex case-insensitive, and limit defaults to 50. Annotations already indicate safe, read-only operation, so no contradiction.

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 well-structured with clear sections (Args, Returns, Examples) and a front-loaded purpose sentence. Every sentence adds value, and there is no redundancy.

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 two parameters and no output schema, the description fully explains input semantics, return format, and provides examples. No gaps in understanding for an AI agent.

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% (filter has description in schema, limit has none). The description adds meaning: filter is regex case-insensitive, limit is maximum with default 50. Examples clarify usage. Slight deduction as filter could be more explicitly tied to URI.

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 clearly states the tool lists available classes in the ontology with instance counts, using a specific verb and resource. It distinguishes from siblings like explore_ontology or search_concepts by emphasizing instance counts and ordering.

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 clear context for when to use (listing classes with counts) but does not explicitly mention when not to use or offer alternatives among siblings. However, the specificity 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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