SeaWeb

search_restaurants

Read-onlyIdempotent

Search restaurants by natural-language intent. location: neighborhood filter (e.g. "Mission", "Marina"); empty (default) = no filter, all SF. goal: discover|book (optional).

Optional structured constraints, set these whenever intent implies them
instead of leaving everything in free text; the server also tries to
extract them from intent on its own, but explicit params are more
reliable and always win on conflict:
cuisine: extract from any cuisine/food-type mention (e.g. "italian food",
  "thai place", "sushi"), pass the cuisine word itself, e.g. "italian".
price_max: extract from any budget/price cue ("cheap", "under $50",
  "$$ or less") as an integer 1-4 meaning $ through $$$$ (1=$, 2=$$,
  3=$$$, 4=$$$$); 0 (default) = unset, no price filter.
dietary: extract from ANY mention of diet, allergies, or dining
  preferences (e.g. "my wife is vegetarian" -> ["vegetarian"], "gluten
  allergy" -> ["gluten-free"]). Bare and "-options"-suffixed forms both
  match (e.g. "vegan" matches a restaurant tagged "vegan-options"), so
  either is fine, prefer values from this set: vegan, vegan-options,
  vegetarian, vegetarian-options, gluten-free-options, dairy-free-options,
  organic, plant-based-milk, fair-trade. This is a HARD filter, every
  listed value must be satisfiable by a returned restaurant, never
  relaxed.
party_size: extract from any group-size mention ("for 6", "party of 4",
  "just the two of us" -> 2). 0 (default) = unset.
bookable: True only when the caller specifically needs a restaurant with
  a live booking link (e.g. "somewhere I can book right now"). False
  (default) means UNFILTERED, it does NOT mean "must not be bookable";
  there is no way to require a non-bookable restaurant through this
  param.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`goal`	No
`intent`	Yes
`cuisine`	No
`dietary`	No
`bookable`	No
`location`	No
`price_max`	No
`party_size`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.3/5.0

Behavior4/5

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

Builds on annotations (read-only, idempotent) by explaining edge cases like bookable's false meaning unfiltered, and dietary being a hard filter. 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.

Conciseness4/5

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

Well-structured with a clear opening, then organized parameter guidance. Slightly lengthy but every part is informative; minimal redundancy.

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?

Covers all necessary aspects: purpose, parameters with extraction rules, and behavioral nuances. Output schema exists, so return values need not be described.

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

Parameters5/5

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

The description compensates for 0% schema coverage by thoroughly explaining each parameter's semantics, extraction rules, and allowed values, adding significant value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it searches restaurants by natural-language intent with location and goal filters. It is distinct from siblings like search_salons and filter_restaurants, but does not explicitly contrast with them.

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

Usage Guidelines4/5

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

Provides explicit guidance on when to set structured parameters vs leaving in free text, noting that explicit params are more reliable. Does not address when to use this tool over alternatives like 'search' or 'filter_restaurants'.

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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