overture-maps

overture_summarize

Read-onlyIdempotent

Count Overture Maps features intersecting a bounded area; filter by attributes and optionally group or aggregate properties and geometry metrics.

Instructions

Count all dataset features intersecting a bounded area, optionally group by a column.

    Accepts exactly the same area/attribute conditions as search. Group by up to 3 scalar
    property paths; top 50 groups with other_count. Aggregate sum/avg/min/max/count/
    count_distinct of properties or @area_m2/@length_m/@distance_m (needs center).
    Geometry metrics default to clipped scope; circles use a 128-segment clip polygon.
    Counts and property aggregates are over whole intersecting records, not prorated.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	No
`theme`	Yes
`bounds`	No
`center`	No
`filters`	No
`polygon`	No
`release`	No
`category`	No
`group_by`	No
`radius_m`	No
`aggregations`	No
`feature_type`	Yes
`clip_geometry`	No
`feature_class`	No
`min_confidence`	No

Output Schema

TableJSON Schema

Name	Required	Default
`data`	Yes
`scope`	No
`theme`	No
`source`	Yes
`license`	Yes
`release`	Yes
`warnings`	No
`next_cursor`	No
`feature_type`	No
`attribution_url`	No	https://docs.overturemaps.org/attribution/

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Oct 4, 2026

TDQS

A3.6/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety bar is low, and the description adds genuinely non-obvious behavior: group results are truncated to the top 50 with an other_count bucket, counts/aggregates are over whole intersecting records rather than prorated, and circles are clipped with a 128-segment polygon. These are exactly the traits an agent cannot infer from annotations.

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?

Six short sentences, front-loaded with the core purpose before the eligibility rules and aggregation details. Dense but every sentence carries information; the phrasing is terse to the point of clipped, which slightly hurts readability but not economy.

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

Completeness3/5

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

An output schema exists, so return values need no prose, and the description covers grouping/aggregation behavior well. However, for a 15-parameter tool with zero schema descriptions, the omission of most scalar filters (theme, feature_type, release, category, min_confidence on top of the two required ones) leaves an agent guessing at key inputs.

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 0% across 15 parameters, so the description carries real burden and does explain group_by (up to 3 scalar property paths), aggregations (the op set plus special @area_m2/@length_m/@distance_m fields requiring center), and clip_geometry's default scope. It says nothing about theme, feature_type, name, release, category, feature_class, min_confidence, or radius_m units/limits, so the compensation is only partial.

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 opening sentence gives a specific verb and resource: counting dataset features intersecting a bounded area, with optional grouping. It implicitly separates itself from overture_search (which returns records, not counts), though it never names the sibling outright. Clear enough that an agent knows this is an aggregation tool.

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?

"Accepts exactly the same area/attribute conditions as search" establishes a relationship to a sibling and implies this is the counting/aggregating counterpart, but it never states when to pick this over overture_search, overture_nearest, or overture_spatial_join. Usage is implied rather than directed.

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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