Skip to main content
Glama
gander-mcp

OpenStreetMap Tagging Schema MCP Server

by gander-mcp

Get Preset Details

get_preset_details

Fetch detailed OpenStreetMap preset data—required and suggested tags, geometry types, fields, options, and parent/child links—to tag a specific feature type correctly.

Instructions

Get complete detailed information about a specific OpenStreetMap preset. Presets are feature templates that define standard tagging patterns (e.g., the 'Restaurant' preset defines amenity=restaurant and lists fields like cuisine, outdoor_seating, etc.). Returns comprehensive preset information including: display name, all required and suggested tags, supported geometry types, complete list of fields, field types and options, and parent/child preset relationships. Use this to understand exactly how to tag a specific feature type, learn what fields are available for a preset, or get example values for proper tagging.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
presetIdYesThe preset identifier to retrieve (e.g., 'amenity/restaurant', 'highway/residential', 'natural/tree'). Preset IDs follow the pattern 'key/value' corresponding to the primary tag. Use search_presets to find preset IDs.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden, and it does disclose the return contents in detail (display name, required/suggested tags, geometry types, fields, field types/options, parent-child relationships). It does not mention failure behavior for an unknown preset ID or whether results are cached/static, which is the remaining gap for a read tool.

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?

Front-loaded with the core action, followed by a definition and an enumerated return list. The parenthetical 'Restaurant' example is a useful concrete anchor rather than filler, though the sentence is somewhat long.

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?

There is no output schema, so the description must explain the return payload — and it enumerates exactly what comes back and why an agent would want it. Combined with the fully documented input parameter, an agent has everything needed to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the single parameter already documents the ID pattern and examples, so the description adds no parameter-level meaning beyond the schema. Per the baseline rule for high coverage, a 3 is appropriate.

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?

States a specific verb and resource ('Get complete detailed information about a specific OpenStreetMap preset') and then defines the domain object itself, so an agent unfamiliar with OSM presets still understands what it retrieves. It is clearly distinguishable from search_presets, which returns IDs rather than full detail.

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?

Gives three concrete use cases: understand how to tag a feature type, learn which fields a preset supports, and get example values for tagging. It does not state when NOT to use it or explicitly contrast against siblings like get_tag_values or search_presets, so it stops short of full routing guidance.

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