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Leviai-ai

AI Compliance Calendar MCP Server

by Leviai-ai

compliance_calendar

Stay compliant with AI regulations by retrieving upcoming deadlines and milestones across jurisdictions. Filter by priority, regulation, or jurisdiction to track critical dates.

Instructions

Get upcoming compliance deadlines and milestones. Shows critical dates for AI regulations across jurisdictions with priority levels and requirements.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
priorityNoFilter by priority level: critical, high, medium, low
days_aheadNoNumber of days to look ahead (default: 365)
regulationNoFilter by specific regulation ID
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 bears the full behavioral burden, and it discloses little beyond output content. It does not mention the default look-ahead window, whether deadlines recur, pagination, ordering, or any auth requirements. Saying it 'shows critical dates with priority levels and requirements' is a content hint, not a behavioral disclosure.

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 no filler, and the core purpose is front-loaded in the first sentence. The second sentence adds some value by naming what the results contain, though it is somewhat redundant with 'compliance deadlines'.

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 simple, optional-parameter read tool with 100% schema coverage and no output schema, the description is adequate: it covers purpose, scope, and result content. It is thin on behavioral traits (no annotations supplied), which leaves the agent without guidance on default windows or result volume.

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 (priority, days_ahead, regulation, jurisdiction) are already documented in the schema. The description adds no parameter syntax or 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 pairs a clear verb ('Get') with a specific resource ('upcoming compliance deadlines and milestones') and clarifies the domain (AI regulations across jurisdictions). It is distinguishable from siblings like get_regulations or regulation_summary by its temporal focus, but it never names or explicitly contrasts with any sibling.

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 statement of when to use this tool versus alternatives such as policy_updates, get_regulations, or regulation_summary, nor any prerequisites or exclusions. The only usage signal is the implied 'upcoming dates' context, which the agent must infer.

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