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Leviai-ai

AI Compliance Calendar MCP Server

by Leviai-ai

policy_updates

Retrieve recent AI policy changes and regulatory updates across global jurisdictions, filtered by impact level, regulation, jurisdiction, and time range.

Instructions

Get recent policy changes and regulatory updates in AI governance. Track the latest developments across jurisdictions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
days_backNoNumber of days to look back (default: 90)
regulationNoFilter by specific regulation
impact_levelNoFilter by impact level: high, medium, low
jurisdictionNoFilter by jurisdiction (US, EU, UK, CA, CN, SG)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It implies a read operation but says nothing about return format, pagination, data sources, refresh cadence, or whether results are limited to a default window — all meaningful gaps for a 'latest updates' feed.

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 short sentences with the core purpose front-loaded. The second sentence largely restates the first ('latest developments' vs 'recent policy changes'), so it earns less than a full 5.

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

Completeness3/5

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

For a 4-param, no-required-filter read tool with no annotations and no output schema, the description covers what it does but not what comes back or how it differs from near-identical siblings. It is minimally viable but leaves real gaps an agent would need filled.

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%, and all four filters (days_back, regulation, impact_level, jurisdiction) are documented in the schema itself. The description only faintly gestures at jurisdictions ('across jurisdictions') and adds no syntax, default, or filtering semantics beyond the schema, 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 names a clear verb (Get) and resource (recent policy changes and regulatory updates in AI governance), so an agent knows what it retrieves. However, it does not differentiate itself from siblings like get_regulations or regulation_summary, which sound like they could return overlapping content.

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

Usage Guidelines2/5

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

There is no explicit when-to-use guidance and no mention of alternatives. 'Track the latest developments' weakly implies a recency/timeliness focus, but with three related siblings (get_regulations, regulation_summary, compliance_calendar) the agent gets no signal for choosing this tool over them.

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