get_salon
Return the full schema.org page for a salon (profile + meta). salon_id and entity_id are aliases; pass either.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| salon_id | No | ||
| entity_id | No |
Return the full schema.org page for a salon (profile + meta). salon_id and entity_id are aliases; pass either.
| Name | Required | Description | Default |
|---|---|---|---|
| salon_id | No | ||
| entity_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only and idempotent behavior, and the description adds value by explaining that salon_id and entity_id are aliases and that the return is the full schema.org page. This goes beyond the annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no wasted words. It front-loads the purpose and immediately provides practical alias guidance, achieving excellent conciseness and structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple get-by-id tool with read-only annotations and no output schema, the description adequately conveys the return value and parameter relationship. It could mention error behavior (e.g., not found) but is otherwise complete for its complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no property descriptions, so the description's statement that the two parameters are aliases partially compensates. However, it does not define the ID types or how they are obtained, leaving some ambiguity given the 0% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the full schema.org page for a salon, specifying both profile and meta. This distinguishes it from siblings like get_restaurant and search_salons by focusing on a specific resource and action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for what the tool does but does not explicitly state when to use it over alternatives. It lacks exclusions or guidance such as 'use search_salons to find IDs first', so usage is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.