waysway

update_user_profile_from_model

Preferred path for profile extraction.

The external model should:
- read the user's natural language message
- decide which STANDARD UserProfile fields are supported by evidence
- send ONLY those fields here

HARD RULES:
- only standard UserProfile fields are allowed
- do NOT invent new fields
- do NOT put city here
- do NOT put checkin_date or checkout_date here
- omit uncertain fields instead of guessing
- numeric budget constraints usually belong to search_hotels price_min/price_max

GOOD EXAMPLE:
{
    "relationships": "solo",
    "travel_purpose": "leisure",
    "hotel_style": "luxury",
    "facilities_like": "breakfast, gym",
    "surroundings_like": "downtown"
}

BAD EXAMPLE:
{
    "city": "New York",
    "checkin_date": "2023-04-01",
    "checkout_date": "2023-04-05",
    "budget": 100
}

Input Schema

TableJSON Schema

Name	Required	Description	Default
`merge`	No
`session_id`	Yes
`extracted_user_profile`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedApr 27, 2026

TDQS

A4.6/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses allowed fields, forbidden fields, and the rule to avoid guessing. However, it does not describe merge/overwrite behavior or what happens when invalid fields are sent, leaving some behavioral ambiguity.

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 well-structured with clear sections (HARD RULES, GOOD EXAMPLE, BAD EXAMPLE). Every sentence adds value, and the examples are compact yet informative. Nothing is redundant.

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

Completeness5/5

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

Despite no annotations, the description fully compensates by explaining the expected input format, constraints, and providing a concrete example. It is complete enough for an agent to invoke the tool correctly without external context.

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

Parameters5/5

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

Schema description coverage is 0%, but the description gives extensive detail for the main 'extracted_user_profile' parameter, including a GOOD EXAMPLE and a BAD EXAMPLE. It clarifies that only standard UserProfile fields are allowed, which is critical given the schema's 'additionalProperties: true'. The other parameters (session_id, merge) are self-explanatory.

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 tool is the 'Preferred path for profile extraction' and describes the workflow of sending standard UserProfile fields. It distinguishes itself from other tools by focusing on model-driven extraction, but it does not explicitly name alternatives or contrast with 'update_user_profile'.

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 when-to-use guidance ('Preferred path'), what to include, and multiple hard rules (e.g., 'do NOT put city here', 'omit uncertain fields'). It also directs numeric budget constraints to 'search_hotels price_min/price_max', clearly indicating an alternative tool.

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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TDQS

B3.4/5.0

Disambiguation4/5

Most tools have clear distinct purposes, though 'update_user_profile' and 'update_user_profile_from_model' serve similar functions with different input methods, which could cause confusion. Overall, the boundaries are generally clear.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case, such as 'search_hotels', 'update_stay_dates', and 'get_hotel_details'. No naming style mixing or irregularities.

Tool Count5/5

With 20 tools covering hotels, homestays, flights, activities, restaurants, profile management, and session handling, the count is well-scoped for a travel assistant server. Each tool serves a distinct function without bloat.

Completeness4/5

The tool set covers core travel planning needs: search, details, offers, and profile management. However, it lacks a tool to actually confirm or book an offer, which is a noticeable gap for a complete booking flow.

Resources

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