Skip to main content
Glama

Yenta Directory

Search venues

search_venues
Read-onlyIdempotent

Search published venues by name or description, optionally filtered by city, cuisine, price band, neighbourhood, whether the venue takes reservations, proximity (near a lat/long within a radius, nearest first) and opening hours (open on a given day/time, or open now) — all HARD filters that exclude on a miss, plus a HARD experiential-facet filter (facets, ':' tokens from facet_vocabulary) — with prefer_cuisine, prefer_neighbourhood, avoid and prefer_facets as SOFT preferences that only rank a venue up or down and never drop it; each result carries the venue's capabilities and, when it is live, its mcp_endpoint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
atNoA 24-hour HH:MM time; with open_on, require the venue be open then.
cityNoOnly return venues in this city (case-insensitive); call list_cities for the exact values this directory knows.
nearNoOnly return venues within radius_km of this 'latitude,longitude' point, nearest first, each with a distance_km; venues with no coordinates are excluded.
avoidNoSoft preference: venues matching these cuisine/neighbourhood tokens rank LOWER; not dropped.
limitNoMaximum number of items to return.
queryNoText to match against venue names and descriptions; omit to list all.
cursorNoNumber of items to skip; pass `next_cursor` from the previous page.
facetsNoHARD experiential-facet filter: each token is '<facet_type>:<value>' (e.g. 'dietary:vegetarian', 'vibe:romantic'). A venue must match every facet_type given (values OR within a type, types AND across); a miss EXCLUDES it. Call facet_vocabulary for the legal types and values.
cuisineNoOnly return venues with this cuisine (case-insensitive exact match).
open_onNoOnly return venues open on this weekday (optionally at 'at'); venues with no published hours are excluded.
open_nowNoOnly return venues open (true) or closed (false) at their own local time now; venues with no timezone or hours are excluded.
radius_kmNoSearch radius in km around 'near' (default applied when omitted).
price_bandNoOnly return venues in this price band, e.g. '$$' (case-insensitive).
neighbourhoodNoOnly return venues in this neighbourhood (case-insensitive exact match).
prefer_facetsNoSOFT experiential-facet preference, same '<facet_type>:<value>' tokens as facets: a venue that declares them ranks higher but is NEVER dropped. Call facet_vocabulary for the legal types and values.
prefer_cuisineNoSoft preference: venues of these cuisines rank higher; non-matches are NOT dropped.
accepts_reservationsNoFilter by whether the venue takes reservations (has a live booking tool or a booking link); omit for both.
prefer_neighbourhoodNoSoft preference: venues in these neighbourhoods rank higher; non-matches are NOT dropped.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemNo
itemsNo
reasonNo
sourceNoFR-13: which approved revision an answer was read from, and how fresh. ``source`` is ``approved-content-revision:<id>`` for every read that answers from one venue, and the literal ``directory`` for the two cross-venue reads, which have no single revision to cite.
statusYes
paginationNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

The description extensively discloses behavior beyond the annotations: filters exclude non-matches, soft preferences never drop results, venues with no coordinates or published hours are excluded, and each result carries capabilities plus mcp_endpoint when live. This is far richer behavioral disclosure than the readOnly/idempotent/destructive annotations alone.

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 long run-on sentence, which hurts readability, but every clause carries distinct, useful information and the core purpose is front-loaded. It could be broken into shorter sentences without losing content, but it has no filler.

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 complex 18-parameter search tool, the description is remarkably complete: it explains filter semantics, exclusion criteria, result payload highlights, and vocabulary references. With a full output schema and strong annotations, nothing necessary for correct invocation is missing.

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?

The input schema already provides 100% parameter descriptions, but the description adds meaning beyond them by framing the hard/soft distinction, including facet token syntax and aggregation behavior, and explaining that soft parameters rank-only and never drop. This exceeds baseline because it resolves ambiguity the schema alone leaves open.

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 states a specific verb and resource ('Search published venues') and enumerates the many supported axes, with explicit hard-filter versus soft-preference semantics. This clearly differentiates it from siblings like search_menus or get_venues.

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?

The description gives clear context for when this tool is appropriate — searching venues by name/description and applying filters — and precisely explains which filters are exclusive versus preferential. However, it doesn't explicitly say when to prefer alternatives like get_venues or search, leaving the routing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources