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Restaurant Reservation MCP Server

make_reservation

Book restaurant reservations on Resy or OpenTable by providing restaurant details, party size, date, and time slot. Direct booking for Resy, booking URL for OpenTable.

Instructions

Book a reservation at a restaurant. For Resy, completes booking directly. For OpenTable, returns a booking URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
restaurant_idYesRestaurant ID
platformYesPlatform
slot_idYesTime slot ID from check_availability
party_sizeYesNumber of guests
dateYesReservation date (YYYY-MM-DD)
Behavior3/5

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

With no annotations, the description carries full burden. It discloses key behavioral traits: platform-dependent outcomes (completes booking vs. returns URL) and dependency on 'check_availability' for slot_id. However, it doesn't cover permissions, error handling, or mutation effects, leaving gaps for a write operation.

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?

Two sentences, zero waste: first states purpose, second adds critical platform-specific behavior. Front-loaded with essential information, no redundant details. Every sentence earns its place efficiently.

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?

Given no annotations and no output schema, the description is incomplete for a 5-parameter write tool. It covers purpose and platform behaviors but lacks details on return values, error cases, or full behavioral context. Adequate as a minimum viable description 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 description coverage is 100%, so the schema already documents all parameters. The description adds minimal value beyond schema: it hints at slot_id's source ('from check_availability') and platform behaviors, but doesn't explain parameter interactions or semantics. Baseline 3 is appropriate as schema does heavy lifting.

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?

The description clearly states the verb ('Book') and resource ('reservation at a restaurant'), and distinguishes between two platform behaviors (Resy completes booking, OpenTable returns URL). This is specific and differentiates it from siblings like 'check_availability' or 'cancel_reservation'.

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 description implies usage by mentioning platform-specific behaviors, but doesn't explicitly state when to use this tool versus alternatives like 'snipe_reservation' or prerequisites. It hints at context with 'from check_availability' for slot_id, but lacks clear when/when-not guidance.

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

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