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rteina

geoparquet-mcp

by rteina

geoparquet_preview_rows

Preview sample records from a remote GeoParquet dataset to see actual values, categories, and nulls before writing filters or queries.

Instructions

Return the first few rows of a dataset, so you can see what the values actually look like.

WHEN TO USE IT. After geoparquet_describe_source tells you a column exists and before you filter on it, to learn how it is populated: what a category string looks like in practice, whether a field is mostly null, how an address is spelled. Guessing a filter value and getting zero features back costs more than one preview.

This is NOT a spatial question. The rows are whatever the file stores first, in no geographic order and in no ranking — do not read them as "the most important places" or "places near anywhere". To ask where things are, use geoparquet_filter_spatial or geoparquet_find_nearest.

PARAMETERS. source: dataset name. columns: column expressions to return, for example ["id", "names.primary", "confidence"]. Omit for the dataset's default projection. limit: how many rows, 1 to 100. Ten is usually enough to see the shape.

WHAT COMES BACK. rows as plain records, columns_returned naming the keys, and the scan block. The read stops at the first row group of the first part file, so the cost does not grow with the dataset.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
sourceNooverture_places
columnsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A5/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so thoroughly. It discloses that rows come in file order, are not ranked or spatially significant, that reads stop at the first row group for cost predictability, and what the response contains. This goes well beyond a basic one-line definition.

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 organized into clear labeled sections, front-loaded with the core purpose, and every sentence adds practical value. Even the cost rationale and non-spatial warning serve important decision-making purposes.

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 modest complexity, the description covers when to use it, how each parameter behaves, what the return payload includes, and the operational cost characteristics. Nothing an agent needs to correctly call and interpret this tool is missing.

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%, but the description's PARAMETERS section explains each parameter meaning, provides a concrete example for columns, and advises on the limit range and typical values. It fully compensates for the schema's lack of descriptions.

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: 'Return the first few rows of a dataset'. It clearly differentiates itself from spatial sibling tools, explicitly saying this is NOT a spatial question and directing to geoparquet_filter_spatial or geoparquet_find_nearest for spatial queries.

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?

Provides an explicit 'WHEN TO USE IT' section: after geoparquet_describe_source and before filtering, to inspect real values. It also states when NOT to use it and names the alternative tools, leaving no ambiguity about its intended role.

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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