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ics-mcp

by mieweb

search_events

Find calendar events by matching text in their title, location, organizer, or description, with optional start and end dates to narrow the results.

Instructions

Search events whose title, location, organizer, or description matches a case-insensitive substring.

Args: query: Text to look for. start: Optional range start. Defaults to 30 days ago. end: Optional range end. Defaults to 180 days ahead. include_description: Include full body text in results. Default False.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNo
queryYes
startNo
include_descriptionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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 does disclose useful traits: matching semantics (case-insensitive substring), the fields searched, the default time window (30 days back to 180 days forward), and that include_description controls whether body text is returned. It does not disclose result limits, sorting, pagination, or the accepted format for start/end strings.

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?

The first sentence front-loads the core purpose, and the Args block is compact and scannable. Nothing is wasted, though the default values are repeated from the schema and the stop/end format gap means the space could have been used more information-densely.

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?

An output schema exists, so return-value explanation is unnecessary and correctly omitted. However, for a search tool with no annotations, missing start/end format, result limits, ordering, and the search-vs-list relationship to siblings leaves real gaps for an agent trying to invoke it correctly.

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 description coverage is 0%, so the description must compensate, and it largely does: it explains that query is matched text, that start/end are optional range bounds with concrete defaults (30 days ago, 180 days ahead), and that include_description defaults to False. The one gap is that start/end formats (e.g., ISO 8601) are not specified despite being typed only as generic strings.

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 specific verb (search) and resource (events) and enumerates the matched fields (title, location, organizer, description) plus matching semantics (case-insensitive substring). It is clear what the tool does, but it never distinguishes itself from siblings like list_events or list_upcoming, leaving the search-vs-list choice implicit.

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 or when-not-to-use guidance, and no mention of the sibling tools (list_events, list_upcoming, list_today) that overlap in purpose. The only usage signal is the word 'search' in the name and description, which an agent must infer means filtered lookup rather than enumeration.

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