SeaWeb

compare_search

Read-onlyIdempotent

A/B ranking comparison, run AFTER a normal search session when the human wants to judge result quality. Ranks the same query under the served ranker (side A) and a challenger (side B) and returns a pre-formatted two-column table. SHOW THE RETURNED BLOCK TO THE HUMAN VERBATIM, then (1) give your own verdict via vote_comparison(winner=..., judged_by="agent", query=..., track_b=...) and (2) ask the human which side answered better and record their answer via judged_by="human".

Input Schema

TableJSON Schema

Name	Required	Default
`intent`	Yes
`track_b`	No	A
`location`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds beyond that by detailing that it ranks the query under two rankers, returns a pre-formatted table, and instructs the agent on how to use the result (show verbatim, vote). 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph but efficiently uses four sentences to cover purpose, prerequisite, and workflow. It is front-loaded with the main action. Slightly verbose, but no unnecessary repetition.

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?

For a tool with moderate complexity (A/B comparison), the description covers purpose, prerequisites (after search), expected output (table), and follow-up actions (vote). Output schema exists, so return values are handled separately. The description is complete for an agent to understand the tool's role in a session.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% (no parameter descriptions in schema). The description does not explain any of the three parameters (intent, track_b, location). While context implies 'intent' relates to the search query, no explicit meaning is added, leaving the agent to infer. This is a significant gap.

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?

The description clearly states this tool performs an A/B ranking comparison after a normal search session, explicitly identifying the verb (run/rank) and resource (comparison). It distinguishes from sibling tools like search and vote_comparison by specifying the timing and the resulting two-column table.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use guidance ('run AFTER a normal search session'), explains the workflow (show block verbatim, then record verdict via vote_comparison), and mentions the alternative vote_comparison for recording judgments. This fully meets the criteria for when vs. alternatives.

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.

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