SeaWeb

teamwork_preview

Read-onlyIdempotent

Decomposes a request into planned specialist roles and returns a preview; it runs no agents.

Decomposes natural language requests into planned subtasks and returns a preview with specialist roles.

STRICT POLICY: SeaWeb does not perform bookings, reservations, or payment
transactions (booking rail retired 2026-08-04). Any booking attempts are
immediately refused with a booking_retired error.

task: Natural language goal or query for the agent team.
max_agents: Maximum number of specialist roles to plan (default 4, range 1-5).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`task`	Yes
`max_agents`	No

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already mark readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by stating it runs no agents and includes a critical STRICT POLICY about booking refusal with a specific error code. This goes beyond the annotations and informs the agent of important behavioral constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The description is mostly concise but contains redundancy: the first two sentences repeat nearly the same information ('Decomposes a request into planned specialist roles...' and 'Decomposes natural language requests into planned subtasks...'). The param list is well-structured, but the repetition could be trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description explains the purpose and policy but lacks details on the return format (what does the preview contain? a list of roles and subtasks? any other metadata?). With no output schema, this gap affects the agent's ability to interpret results. The policy is a strong addition, but completeness is moderate.

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

Parameters4/5

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

Schema coverage is 0%, but the description provides clear explanations for both parameters: 'task' is a natural language goal or query, and 'max_agents' gets a default and range (1-5) not present in the schema. This compensates for the lack of schema-level descriptions and adds practical constraints.

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 the tool decomposes a request into planned specialist roles and returns a preview, with the explicit note that it runs no agents. This verb-resource-action is specific, and the sibling tools include no similar preview tool, so it is well-differentiated.

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

Usage Guidelines2/5

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

The description provides a strong policy warning against booking attempts but offers no guidance on when to use this tool versus alternatives among the 40+ siblings. It does not mention prerequisites, follow-up tools, or scenarios where a preview is appropriate versus a full execution tool.

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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