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Glama

POS.DO MCP Server

search_restaurants

Search restaurants in a specific city. Filter by cuisine type, price range, and features like outdoor seating, WiFi, delivery. Returns up to 20 results with name, rating, cuisine, price range, address, coordinates, photo, amenities, and current Flow offers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYesCity name. Examples: 'Madrid', 'New York', 'Milano', 'Barcelona'.
limitNo
queryNoFree-text search: cuisine, occasion, features (e.g. 'romantic italian terrace').
cuisineNoCuisine filter. Examples: 'italian', 'mexican', 'sushi', 'spanish', 'indian'.
featuresNoComma-separated feature filters: terrace, wifi, parking, delivery, accessible.
price_maxNoMax price level 1=budget, 4=fine dining.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

No annotations provided, so the description bears the full burden. It discloses that the tool returns up to 20 results with specific fields (name, rating, cuisine, etc.), which is transparent. It does not mention rate limits or authorization, but for a read-only search this is acceptable.

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

Conciseness5/5

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

The description is two clear sentences with no unnecessary words. It front-loads the core purpose and efficiently lists filters and return fields.

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?

Despite no output schema, the description enumerates return fields. It covers all parameters meaningfully, specifies result limit, and provides enough context for an AI agent to use the tool correctly given the tool's moderate complexity.

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 83%, and the description adds value by explaining filters like 'comma-separated features' and 'free-text search for cuisine/occasion'. It also specifies the default limit and max results, enhancing the schema's information.

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 it searches restaurants in a specific city with multiple filters. It distinguishes itself from sibling tools like find_restaurants_near_landmark and get_restaurant_details by specifying city-based search and return fields.

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 indicates using this tool for searching restaurants by city and filters. It does not explicitly state when not to use it or compare with siblings, but the context of city-based search is clear.

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

A4/5.0
Disambiguation5/5

Each tool has a distinct purpose: reservation booking, landmark-based search, cultural context, flow offers, restaurant details, reviews, and general search. No overlapping functionalities.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern (e.g., book_flow_reservation, get_restaurant_details), making it easy to infer actions.

Tool Count5/5

Seven tools cover the core domain (search, details, reviews, offers, booking, cultural context) without being excessive or insufficient.

Completeness4/5

Core workflows are covered, but the set lacks reservation cancellation or update functionality, which is a minor gap for a booking system.