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ogSINGH

OpenStreetMap MCP Server v2

by ogSINGH

find_nearby_places

Locate points of interest and amenities around a latitude/longitude by radius and category, returning nearby places grouped for location-based recommendations.

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it partially meets it: it discloses the default radius and that results are grouped by category, and the 'discover/search' framing implies a read-only operation. However, it says nothing about permissions, rate limits, error behavior, or pagination, so key behavioral traits remain undisclosed.

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?

Purpose is front-loaded and the Args section is justified given 0% schema coverage. The Returns block duplicates the existing output schema and the multi-sentence framing paragraph is somewhat padded, but nothing is seriously wasteful.

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?

For a 5-parameter read tool, the definition covers purpose, every parameter, and the result shape, which is enough to invoke it correctly. It is slightly incomplete on routing between this and the specialized sibling find_* tools, which an agent would still have to infer.

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 coverage is 0%, so the description must compensate and it does: it defines all five parameters, gives the radius default (1000m/1km), explains that categories accepts OSM-style values with concrete examples (['amenity','shop','tourism']) and that omitting it searches common categories, and clarifies limit is a *total* result cap. This adds meaning far beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource ('Discover points of interest and amenities near a specific location') and elaborates that results are grouped by category and subcategory. It clearly conveys the operation, but it never contrasts itself with generic siblings like explore_area or search_category, so an agent gets no explicit differentiation.

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

Usage Guidelines3/5

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

It names use contexts ('location-based recommendations, neighborhood analysis, and proximity-based decision making'), which implies when the tool is appropriate, but offers no when-not guidance or alternatives. Notably, it gives no hint about when to prefer this general search over narrow siblings such as find_schools_nearby, find_ev_charging_stations, or find_parking_facilities.

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