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Leviai-ai

AI Compliance Calendar MCP Server

by Leviai-ai

get_regulations

Search and filter AI regulations by jurisdiction, category, and status; returns requirements, deadlines, and penalties for compliance tracking.

Instructions

Search and filter AI regulations by jurisdiction, category, and status. Returns comprehensive regulatory information including requirements, deadlines, and penalties.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchNoSearch term to filter regulations by name or description
statusNoFilter by status: active, enacted, draft, bill, guidance
categoryNoFilter by category: privacy, safety, transparency, governance, risk-management, algorithmic-accountability
jurisdictionNoFilter by jurisdiction (US, EU, UK, CA, CN, SG) - case insensitive

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses that results include requirements, deadlines, and penalties, but says nothing about read-only nature (inferable from 'Search'), result volume, pagination, or rate limits. Partial behavioral coverage for a zero-annotation tool.

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?

Two sentences, front-loaded with the action and filters, and no filler. The second sentence is somewhat general but still earns its place given the absence of an output schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and four optional filters, the description does the right thing by sketching the return payload (requirements, deadlines, penalties). It falls short only on result-size/pagination behavior, which an agent might still need.

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 description coverage is 100%, so all four parameters (search, status, category, jurisdiction) are already documented with allowed values in the schema. The description only restates the filter dimensions and adds no syntax, format, or combination guidance beyond that, so the baseline 3 applies.

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?

The description pairs a specific verb ('Search and filter') with the resource ('AI regulations') and names the filter axes, so the agent knows exactly what the tool does. It does not differentiate itself from siblings like regulation_summary or jurisdiction_compare, which is the only thing keeping it out of the top band.

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

Usage Guidelines3/5

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

Usage is implied by the search/filter framing, but there is no explicit statement of when to reach for this tool versus regulation_summary or jurisdiction_compare, nor any exclusions or prerequisites. Adequate but leaves the agent to infer routing.

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