SeaWeb

search_salons

Read-onlyIdempotent

Search hair salons, barbershops and beauty salons by natural-language intent (e.g. "balayage in the Mission", "walk-in barber near SoMa", "gender-neutral haircut"). Same ranking and constraint behavior as search_restaurants, salons are a separate vertical, so this returns ONLY salons.

location: neighborhood filter (e.g. "Mission District", "SoMa", "The
  Castro"); empty (default) = all SF.
goal: discover|book (optional).
cuisine: reused as the SERVICE-TYPE slot, pass a service word to filter
  (e.g. "color", "balayage", "haircut", "perm", "beard trim").
price_max: budget cue as int 1-4 ($ through $$$$); 0 = unset.
dietary: unused for salons (no dietary tags); leave empty.
party_size: group-size mention ("for 2"); 0 = unset.
bookable: True only when the caller needs a live booking link.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`goal`	No
`intent`	Yes
`cuisine`	No
`dietary`	No
`bookable`	No
`location`	No
`price_max`	No
`party_size`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. Description adds value by explaining ranking behavior (same as search_restaurants) and that it returns only salons, plus parameter reuse details. No contradiction.

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?

Well-structured with first paragraph for purpose and bullet points for parameters. Some redundancy in parameter descriptions (e.g., location repeated), but overall efficient and front-loaded.

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?

Given 8 parameters, 0% schema description, and presence of output schema, the description covers all parameters and adds context on ranking, vertical exclusivity, and parameter reuse. No gaps identified.

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 has 0% description coverage, but description thoroughly explains each parameter: purpose of location, goal, cuisine as service-type, price_max, dietary unused, party_size, bookable, and intent. Fully compensates for missing schema 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?

Clearly states the tool searches for hair salons, barbershops, and beauty salons using natural language. Differentiates from sibling search_restaurants by specifying it's a separate vertical that returns only salons.

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?

Provides examples of natural-language intents and explains when to use 'bookable' parameter. Implicitly contrasts with search_restaurants, but does not explicitly state when not to use this tool or list alternatives beyond the sibling mention.

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

Disambiguation3/5

There is notable overlap among search, search_web, search_restaurants, and search_salons, as well as between filter_restaurants/filter_salons and search with constraints. However, descriptions clarify the intended vertical or corpus, and entity getters are distinct. The overlap is manageable but could cause misselection.

Naming Consistency4/5

Names mostly follow a get_/list_/search_/register_/delete_/submit_/vote_ pattern in snake_case. Minor deviations like 'recall', 'remember', 'research', and 'travel_health' are less predictable but still readable. Overall consistent and clear.

Tool Count2/5

38 tools is on the heavy side for a single MCP server, exceeding the typical well-scoped range. While the server covers multiple subdomains (search, travel disruptions, memory, feedback, research), the sheer number may overwhelm agents and suggests potential consolidation.

Completeness4/5

The tool surface covers core workflows: search and entity retrieval for restaurants/salons, disruption monitoring with standing queries and webhooks (register/list/delete), research submission/polling, and memory/feedback mechanisms. Minor gaps exist (e.g., no cancel for research jobs, no explicit entity list endpoint), but these are workable and do not break typical agent tasks.

Resources

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