@striderlabs/mcp-marriott

select_room

Select a room type and optional rate plan for a hotel reservation after viewing available options.

Instructions

Choose a room type to book. Call this after get_room_options to select a room before checkout.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "hotelId": "<hotelId>",
      "roomCode": "<roomCode>"
    },
    "name": "select_room"
  }
}

Input Schema

Name	Required	Description
`hotelId`	Yes	Hotel property code
`roomCode`	Yes	Room type code from get_room_options
`ratePlanCode`	No	Rate plan code from get_room_options (optional)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 30, 2026

TDQS

A3.6/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only hints at a mutation ("book") but fails to specify authorization needs, side effects (e.g., does it hold the room?), or what happens upon success or failure. This is insufficient for an agent to correctly understand behavior.

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, no filler. The first sentence states the purpose, the second gives usage sequence. Every word earns its place. Ideal conciseness for a tool description.

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 output schema and moderate complexity (3 params, 2 required), the description provides the essential sequential context but omits return value or confirmation details. It is minimally viable but leaves gaps about what the agent can expect after calling the tool.

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% with basic descriptions, but the tool description adds valuable context: roomCode and ratePlanCode come from get_room_options, linking them to a prior step. This goes beyond the schema's standalone descriptions, helping the agent understand data dependencies.

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 clearly states the action "Choose a room type to book" with a specific verb and resource. It also provides sequential context: to be called after get_room_options and before checkout, which distinguishes it from sibling tools like get_room_options and checkout. However, it could more explicitly differentiate from other selection tools.

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

Usage Guidelines4/5

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

The description explicitly says to call this after get_room_options and before checkout, providing clear usage context. It does not mention when not to use or list alternatives, but the sequential guidance is effective for an AI agent.

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

Deploy Server