Skip to main content
Glama

libcal_book

Books a confirmed UNC LibCal study room or computer slot after the user selects an option. Requires explicit date, start time, duration, and user confirmation.

Instructions

Book a specific UNC LibCal slot. BOOKING WORKFLOW (required):

  1. For open-ended requests ("book a room", "max hours", "soonest available") → call libcal_suggest FIRST.

  2. Present numbered options to the user. Explain the recommended option (usually same-day if available).

  3. Only call libcal_book after the user picks an option (or gives explicit date+time).

  4. Never auto-book a far-future date when sooner options exist. Requires user_confirmed=true. Categories: davis-cubes, davis-study-rooms, davis-computers

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesBooking date YYYY-MM-DD (from user choice or libcal_suggest option)
categoryNoSpace category id (default: davis-cubes)
space_idNoLibCal item/space id (from libcal_suggest option if available)
start_timeYesStart time HH:MM (from user choice or libcal_suggest option)
user_confirmedYesMust be true — confirms the user explicitly chose this slot
add_to_calendarNoAdd to Apple Calendar after booking (default true)
duration_minutesYesBooking length in minutes
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It clearly states the user_confirmed=true requirement, the category restrictions, and the anti-far-future-booking guardrail. It could additionally mention side effects like calendar creation or failure behavior, but the key behavioral constraints are well covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is tightly structured with a numbered workflow and no filler. Each sentence contributes necessary operational guidance, and key constraints are front-loaded before the raw category list.

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?

The description covers the critical workflow context: prerequisites, confirmation requirement, category options, and when invocation is appropriate. It leaves some operational details to the schema (duration bounds, optional parameters) and doesn't describe error/return behavior, but for a booking action the core invocation context is complete.

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 coverage is 100%, so the baseline is 3. The description adds value by clarifying that date and start_time come from user choice or libcal_suggest options, that user_confirmed must be true, and by listing valid categories. This goes beyond the schema's basic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool's function: 'Book a specific UNC LibCal slot.' It distinguishes itself from siblings by describing the required prerequisite workflow with libcal_suggest and by limiting scope to confirmed, specific bookings.

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

Usage Guidelines5/5

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

Provides explicit, step-by-step usage guidance: call libcal_suggest first for open-ended requests, present options, only call after user picks an option or gives explicit date/time, and never auto-book a far-future date when sooner options exist. This is strong, actionable routing guidance.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Thespaceblade/unc-libcal-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server