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calendar_ai_assist

Translate plain-language requests into calendar actions: schedule, reschedule, find free time, and summarize events by reading the calendar. Returns executable actions for AI agents.

Instructions

Ask the calendar AI in plain language: schedule, reschedule, find free time, summarize. It reads the calendar and returns executable actions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYesWhat to do, in plain language
timezoneNoIANA name, e.g. Asia/Kolkata
calendarIdYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses that the tool 'reads the calendar and returns executable actions', clarifying that it does not itself apply mutations. It omits permission requirements, rate limits, and what happens to returned actions, so disclosure is partial.

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 tight sentences with the core capability list front-loaded and no filler. The 'plain language' idea is repeated in both the description and the message parameter, a minor redundancy that keeps it just below a 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 3-parameter tool with no annotations and no output schema, the description covers purpose and the read-vs-execute distinction but leaves the return format, error behavior, and calendarId semantics unexplained. Adequate but with clear gaps.

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 coverage is 67%: 'message' and 'timezone' are documented in the schema while 'calendarId' is not. The description adds meaning only for 'message' by emphasizing plain language; it does not compensate for the undocumented calendarId or add format details for timezone, so baseline 3 is appropriate.

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 states a clear verb+resource: it's an AI assistant for calendar operations (schedule, reschedule, find free time, summarize) driven by plain language. The capability list is specific, but it does not explicitly contrast itself with granular siblings like calendar_create_event or calendar_update_event, so differentiation is only implicit.

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?

The phrase 'in plain language' implies the tool is for natural-language, multi-step, or ambiguous calendar requests, which is an implicit usage cue. However, there is no explicit when-to-use vs when-not guidance and no named alternatives among the calendar_* siblings, leaving selection to inference.

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