Google Maps

search_places

Read-only

Call this tool when the user's request is to find places, businesses, addresses, locations, points of interest, or any other Google Maps related search.

Input Requirements (CRITICAL):

text_query (string - MANDATORY): The primary search query. This must clearly define what the user is looking for.
- Examples: 'restaurants in New York', 'coffee shops near Golden Gate Park', 'SF MoMA', '1600 Amphitheatre Pkwy, Mountain View, CA, USA', 'pets friendly parks in Manhattan, New York', 'date night restaurants in Chicago', 'accessible public libraries in Los Angeles'.
- For specific place details: Include the requested attribute (e.g., 'Google Store Mountain View opening hours', 'SF MoMa phone number', 'Shoreline Park Mountain View address').
location_bias (object - OPTIONAL): Use this to prioritize results near a specific geographic area.
- Format: {"location_bias": {"circle": {"center": {"latitude": [value], "longitude": [value]}, "radius_meters": [value (optional)]}}}
- Usage:
  - To bias to a 5km radius: {"location_bias": {"circle": {"center": {"latitude": 34.052235, "longitude": -118.243683}, "radius_meters": 5000}}}
  - To bias strongly to the center point: {"location_bias": {"circle": {"center": {"latitude": 34.052235, "longitude": -118.243683}}}} (omitting radius_meters).
language_code (string - OPTIONAL): The language to show the search results summary in.
- Format: A two-letter language code (ISO 639-1), optionally followed by an underscore and a two-letter country code (ISO 3166-1 alpha-2), e.g., en, ja, en_US, zh_CN, es_MX. If the language code is not provided, the results will be in English.
region_code (string - OPTIONAL): The Unicode CLDR region code of the user. This parameter is used to display the place details, like region-specific place name, if available. The parameter canaffect results based on applicable law.
- Format: A two-letter country code (ISO 3166-1 alpha-2), e.g., US, CA.

Instructions for Tool Call:

Location Information (CRITICAL): The search must contain sufficient location information. If the location is ambiguous (e.g., just "pizza places"), you must specify it in the text_query (e.g., "pizza places in New York") or use the location_bias parameter. Include city, state/province, and region/country name if needed for disambiguation.
Always provide the most specific and contextually rich text_query possible.
Only use location_bias if coordinates are explicitly provided or if inferring a location from a user's known context is appropriate and necessary for better results.
The grounded output must be attributed to the source using the information from the attribution field when available.

Input Schema

TableJSON Schema

Name	Required	Description
`textQuery`	Yes	Required. The text query.
`regionCode`	No	Optional. The Unicode country/region code (CLDR) of the location where the request is coming from. This parameter is used to display the place details, like region-specific place name, if available. The parameter can affect results based on applicable law. For example, "US" for United States. For more information, see https://www.unicode.org/cldr/charts/latest/supplemental/territory_language_information.html. Note that 3-digit region codes are not currently supported.
`languageCode`	No	Optional. The language to request that the summary is returned in. If the language code is unspecified or unrecognized, the summary with a preference for English will be returned. For example, "en" for English. Current list of supported languages: https://developers.google.com/maps/faq#languagesupport.
`locationBias`	No	An optional region to bias the search results to. If an explicit location is in `text_query`, it will be used to bias the search results instead of this field.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`places`	No	Output only. The list of places that are mentioned in the summary.
`summary`	No	Output only. A natural language summary of the search results. The summary may contain zero-based citations like "[0]", "[1]", "[2]" etc. These citations map to the corresponding places in the `places` field.

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds useful behavioral context: language and region codes can affect results, location_bias prioritizes but does not limit results, and attribution must be included. It also explains that location_bias usage should be restricted to certain conditions.

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 long but well-structured with sections, numbered lists, and examples. Every paragraph serves a purpose, though some examples could be trimmed. It remains appropriately sized for a tool with four parameters and complex usage rules.

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

Completeness4/5

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

Given the tool's complexity and the presence of an output schema, the description covers all essential aspects: when to use, parameter semantics, location handling, and attribution. It could mention potential return behavior (e.g., multiple results), but the output schema sufficiently covers return values.

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 description coverage is 100%, but the description adds meaningful examples for each parameter: text_query examples include 'restaurants in New York' and 'SF MoMA', location_bias is shown with explicit JSON formats, and language_code/region_code have format details. This goes beyond the schema's basic descriptions.

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 opens with a clear call-to-action: 'Call this tool when the user's request is to find places, businesses, addresses, locations, points of interest, or any other Google Maps related search.' This provides a specific verb and resource, and it distinguishes from sibling tools like compute_routes and lookup_weather by focusing on place search.

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?

It explicitly states when to use the tool and gives detailed instructions for handling ambiguous locations and when to use location_bias. However, it does not explicitly mention when NOT to use it or point to alternatives, so it lacks the 'when-not/alternatives' element required for a 5.

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

A4.5/5.0

Disambiguation5/5

Each tool has a distinctly different purpose: compute_routes for travel directions, lookup_weather for meteorological data, search_places for free-text place discovery, and resolve_maps_urls/resolve_names for batch converting specific inputs (URLs or exact names) into canonical place IDs. There is no overlap in function that would cause an agent to select the wrong tool.

Naming Consistency4/5

All tools follow a verb_noun snake_case pattern (compute_routes, lookup_weather, search_places, resolve_names, resolve_maps_urls). The use of 'resolve' for two tools is slightly redundant but each targets a distinct input type (names vs. URLs), so the pattern remains predictable and clear.

Tool Count5/5

With five tools, the server is well-scoped for a Google Maps integration. It covers the essential capabilities (routing, weather, place search, and ID resolution) without unnecessary bloat, falling comfortably within the ideal 3-15 tool range.

Completeness4/5

The tool surface covers core map-related tasks: route calculation, weather, place searching, and canonical place ID resolution from both URLs and named locations. A potential gap is lack of a dedicated reverse geocoding tool (lat/lng → address), but lookup_weather's geocoded output partially covers this, and search_places can handle address-like queries. Overall, the domain is well covered with only minor omissions.

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