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find_nearby_places

Identify nearby points of interest and amenities using coordinates and radius. Get categorized results for location-based recommendations and neighborhood analysis.

Instructions

Discover points of interest and amenities near a specific location.

This tool performs a comprehensive search around a geographic point to identify nearby establishments, amenities, and points of interest. Results are organized by category and subcategory, making it easy to find specific types of places. Essential for location-based recommendations, neighborhood analysis, and proximity-based decision making.

Args: latitude: Center point latitude (decimal degrees) longitude: Center point longitude (decimal degrees) radius: Search radius in meters (defaults to 1000m/1km) categories: List of OSM categories to search for (e.g., ["amenity", "shop", "tourism"]). If omitted, searches common categories. limit: Maximum number of total results to return

Returns: Structured dictionary containing: - Original query parameters - Total count of places found - Results grouped by category and subcategory - Each place includes name, coordinates, and associated tags

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
radiusNo
latitudeYes
longitudeYes
categoriesNo
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that results are 'organized by category and subcategory' and details the return structure (original query, total count, grouped results). While it doesn't mention side effects or rate limits, as a search tool it doesn't need to; it adds behavioral context beyond a simple statement.

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 well-structured with a clear summary, paragraphs, Args section, and Returns section. It is slightly longer than necessary but each part earns its place, especially the parameter documentation and return structure explanation.

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 no output schema, the description thoroughly explains the return value structure. All 5 parameters are documented, and the tool's purpose and use cases are covered. It is complete for a search tool of moderate complexity.

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?

Schema description coverage is 0%, and the description compensates fully. Each parameter (latitude, longitude, radius, categories, limit) is explained, including defaults, examples, and behavior when omitted (e.g., 'If omitted, searches common categories').

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 the tool discovers points of interest and amenities near a location, specifying 'comprehensive search around a geographic point.' It distinguishes itself from siblings like find_schools_nearby and find_ev_charging_stations by emphasizing generic POI search and category/subcategory organization.

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 clear usage context: 'Essential for location-based recommendations, neighborhood analysis, and proximity-based decision making.' However, it does not explicitly mention when not to use it or name alternative tools, so it lacks explicit exclusions.

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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