Rate a search result you actually used. Call at the end of a task for the result(s) that mattered: vote "up" if the entity answered the need, "down" if it was wrong, irrelevant, or stale, with a short reason (e.g. "menu was current", "permanently closed"). Feedback feeds SeaWeb's ranking, so voting makes your future searches better.
| Name | Required | Description | Default |
|---|---|---|---|
| vote | Yes | ||
| reason | No | ||
| entity_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description discloses that feedback affects SeaWeb's ranking to improve future searches. Annotations show no destructive or read-only hints, and the description aligns. No contradictions.
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 sentences, front-loaded with the main action, no unnecessary words. Every sentence adds value.
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?
No output schema, so description should explain return value but doesn't. It omits specifying allowed enum for 'vote' and that 'reason' defaults to empty string. Otherwise adequate for a simple feedback tool.
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 coverage is 0%, so description carries the burden. It explains 'entity_id' as the search result, 'vote' as up/down, and 'reason' with examples. However, it does not specify allowed values for 'vote' (e.g., 'up', 'down') or that 'reason' is optional.
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?
Description explicitly states 'Rate a search result you actually used' and defines voting up/down. It clearly distinguishes from sibling 'vote_comparison' by emphasizing feedback on results actually used at task end.
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?
Explicitly says 'Call at the end of a task' and provides criteria for up vs down votes. Does not explicitly exclude alternatives like 'vote_comparison', but the context is well-defined.
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.