Google Maps

resolve_names

Read-only

Resolves a batch list of specific location queries (landmark names or exact addresses) into canonical Google Maps Place IDs.

Input Requirements (CRITICAL):

queries (array of objects - MANDATORY): A list of location queries to resolve. You may specify up to 20 queries.
- Each query object must have:
  - text (string - MANDATORY): The text query representing a specific place name or address to resolve.
    - Examples: 'Googleplex, Mountain View, CA', '1600 Amphitheatre Pkwy, Mountain View, CA', 'Eiffel Tower, Paris'.
location_bias (object - OPTIONAL): Use this to prioritize results near a specific geographic area.
- Format: {"viewport": {"low": {"latitude": [value], "longitude": [value]}, "high": {"latitude": [value], "longitude": [value]}}}
region_code (string - OPTIONAL): The Unicode CLDR region code (two-letter country code, e.g., US, CA) of the user to bias the results.

Instructions for Tool Call:

Specificity (CRITICAL): Queries must represent a specific place name or address. General searches like 'restaurants' or chain names like 'Starbucks' are not supported.
Do NOT call this tool if the downstream tools you plan to invoke already accept raw address or place name strings directly.

Error Handling (CRITICAL):

This is a batch processing tool. A request might return "mixed results" (e.g. some queries resolve successfully while others fail).
The output list of results is guaranteed to map 1:1 with the input queries indices. A failed query will result in an empty Result message (no entity is set) at its corresponding index in the results list.
You MUST check the failed_requests map field in the response to identify which specific query index failed. The key of failed_requests represents the 0-based index of the failed query in the request. Do not assume the entire batch call failed because of a partial failure.

Input Schema

TableJSON Schema

Name	Required	Description
`queries`	Yes	Required. A list of location queries to be resolved. You may specify up to 20 queries.
`regionCode`	No	Optional. An optional region code to bias the resolution results. If specified, the resolution results will be biased towards the entities that are in or near the specified region. This should be a CLDR region code. For example, "US" or "CA". Including `location_bias` or `region_code` often provides better results by narrowing the search space. If both `location_bias` and `region_code` are specified, `location_bias` takes precedence over `region_code`.
`locationBias`	No	Optional. An optional region to bias the resolution results. If specified, the resolution results will be biased towards the entities that are closer to this region. Including `location_bias` or `region_code` often provides better results by narrowing the search space. If both `location_bias` and `region_code` are specified, `location_bias` takes precedence over `region_code`.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`results`	No	Output only. The list of resolved entities from the location queries. Guaranteed to map 1:1 with the request `queries` indices. An empty string at index `i` indicates the resolution failed for that query. If the resolution failed, please check the `failed_requests` field for the error status.
`failedRequests`	No	Output only. A map communicating partial failures. The key is the index of the failed request in the `queries` field. The value is the error status detailing why the resolution failed.

TDQS

A4.9/5.0

Behavior5/5

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

Beyond the readOnlyHint/destructiveHint annotations, the description discloses critical batch behavior: mixed results, 1:1 index mapping, empty Result for failed queries, and the failed_requests map. This is valuable context that annotations alone do not provide.

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 well-structured with clear headings (Input Requirements, Instructions, Error Handling) and bullet points. It is long but every section serves a purpose, and the key purpose statement is front-loaded. The formatting aids readability and agent comprehension.

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?

Given the tool's batch-processing complexity and the presence of an output schema, the description covers all necessary operational aspects: input requirements, param formats, constraints, partial failure handling, and mapping guarantees. It is complete for an agent to invoke correctly.

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?

While schema coverage is 100% and already documents all parameters, the description adds meaningful context with concrete examples for `text`, the exact JSON structure for `location_bias`, and a note on precedence between `location_bias` and `region_code`. This enriches the schema beyond baseline.

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, specific statement: 'Resolves a batch list of specific location queries (landmark names or exact addresses) into canonical Google Maps Place IDs.' This immediately establishes the verb, resource, and scope, and distinguishes it from siblings like search_places and resolve_maps_urls.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance, including a direct 'Do NOT call this tool if...' exclusion and details on supported vs unsupported queries. This gives the agent concrete criteria for when to use this tool versus alternatives or downstream tools.

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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