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Reese-max

Travel Planning MCP

by Reese-max

get_trip_context

Get an aggregated trip view that bundles referenced places, reservations, constraints, and version history, providing AI agents with complete context for travel planning and itinerary updates.

Instructions

Read an AI-friendly aggregate containing the trip plus referenced places, reservations, constraints, and available versions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
trip_idYes
versionNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

C2.8/5.0
Behavior2/5

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

No annotations are provided, so the description must disclose safety and behavioral details. It does not mention whether this is a read-only operation (though 'Read' implies it), or any side effects, limitations, or performance considerations. It does not describe return format or error behavior, which is important for a tool with no output schema. The basic read-only nature is implied but not explicit.

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 description is a single concise sentence that front-loads the main action ('Read an AI-friendly aggregate'). It is efficient and to the point, with no redundancy. It could be improved by adding a brief usage note, but it is appropriately succinct.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is moderately complex (aggregate of multiple entity types), yet the description only summarizes the content. There is no output schema to clarify return structure, and the description does not explain the 'version' parameter or any pagination/limits. Given the richness of the aggregate, an agent would need more details to use it correctly, especially regarding the 'version' parameter and what 'AI-friendly' implies.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning the schema provides no descriptions for the parameters. The tool description does not explain either parameter. 'trip_id' is obvious as an identifier but lacks format or usage details. 'version' is undocumented—its meaning (which version of the trip context?) is unclear, and the description does not clarify. With zero schema coverage and no explanation in the description, this is a significant gap.

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 it reads a trip aggregate including places, reservations, constraints, and versions. It uses a specific verb ('read') and resource ('trip aggregate'), distinguishing it from tools like get_trip, get_place, etc. However, it could be more explicit about how it differs from get_trip, as both seem to fetch trip data.

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 use when you need a comprehensive snapshot of trip context, but does not give explicit when-to-use guidance or contrast with alternatives like get_trip. Sibling tools exist, but the description does not mention them, leaving the agent to infer when this aggregate is preferred.

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