Record an A/B verdict after compare_search. winner: "A", "B", or "tie". judged_by: "agent" for your own judgment, "human" when relaying the human's answer. Pass the same query and track_b the comparison used.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | ||
| reason | No | ||
| winner | Yes | ||
| track_b | No | A | |
| judged_by | No | human |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate a write operation but not destructive. The description adds behavioral context by specifying how judged_by works and the need to pass query and track_b, but does not disclose potential side effects like overwriting or idempotency.
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?
Two succinct sentences that front-load the purpose and then detail parameters. No unnecessary information; every word earns its place.
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 record action, the description covers core behavior and key parameters. It lacks explanation of the return value and validation rules, but given the absence of output schema and the tool's simplicity, it is mostly complete.
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?
With 0% schema coverage, the description adds meaning for winner (allowed values), judged_by (options), and mentions query and track_b. However, the 'reason' parameter is not explained, leaving a gap.
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 records an A/B verdict after compare_search, specifying the winner options and the judged_by field. It distinguishes itself from siblings by being the dedicated tool for recording comparison results.
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 explicitly instructs the agent to use this tool after compare_search and to pass the same query and track_b. While it doesn't explicitly mention when not to use it, the context of siblings makes the usage clear.
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.