Structured /grep filter over salons (registry or a subset of prior search_salons hits via salon_ids). Salon-only vertical.
| Name | Required | Description | Default |
|---|---|---|---|
| salon_ids | No | ||
| price_range | No | ||
| neighborhood | No | ||
| open_tonight | No | ||
| publisher_id | No | ||
| accepts_reservations | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds the context of filtering a 'registry or subset of prior search_salons hits,' which is helpful but does not disclose any additional behavioral traits (e.g., performance, authentication). No contradiction with annotations.
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 very concise—two sentences that immediately convey the tool's purpose. No wasted words. The structure could be slightly improved by bolding key terms, but it is effective.
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?
Given the tool has 6 optional filter parameters and an output schema (not shown), the description is too sparse. It does not explain how filters combine (AND/OR), provide examples, or describe the output beyond being a 'structured' filter. The many sibling tools increase the need for clarity.
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?
Schema description coverage is 0%, so the description should compensate. It only mentions salon_ids in context, leaving the other 5 parameters (price_range, neighborhood, open_tonight, publisher_id, accepts_reservations) with no additional meaning beyond their names and types. This is insufficient for a filter tool.
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 it is a 'structured /grep filter over salons' and mentions it can operate on a subset from search_salons via salon_ids. It distinguishes the tool as a filter, not a search, but could explicitly contrast with search_salons.
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?
No guidance on when to use this tool versus siblings like search_salons or filter_restaurants. The description does not specify prerequisites or exclusions, leaving the agent to infer usage from context.
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.