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gisgp

by io.github.uponex
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validate_geojson

Check whether a GeoJSON file/string is valid — catches broken geometry, malformed structure, self-intersections, and out-of-range coordinates before you try to use the file elsewhere (e.g. "is this GeoJSON valid", "why won't my GeoJSON load", "lint my GeoJSON", "check topology errors"). Validates RFC 7946 structure, topology, and WGS84 coordinate ranges.

Returns a JSON report: {valid, errors, warnings, stats}.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "geojson": "<geojson>"
    },
    "name": "validate_geojson"
  }
}

Input Schema

NameRequiredDescriptionDefault
geojsonYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

There are no annotations, so the description carries the full burden. It discloses the validation scope (RFC 7946 structure, topology, WGS84 ranges) and explicitly states the return format ({valid, errors, warnings, stats}), making behavior predictable. It also implies a non-destructive check without needing annotations.

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 concise (two short paragraphs) and front-loads the core purpose, then adds concrete examples, validation scope, and return shape. Every sentence contributes useful information with no filler.

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?

Despite having only one parameter and no annotations, the description provides enough detail for an agent to know when to invoke the tool, what to pass, and what to expect in return. It is self-contained for this validation use case and does not require supplemental context.

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?

The input schema only defines a single 'geojson' string with no description, and schema description coverage is 0%. The description adds meaning by indicating the input is a GeoJSON file/string, but leaves ambiguity about whether a file path or raw JSON content is expected. This is a moderate compensation for the empty schema.

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 uses a specific verb ('Check whether') and resource ('GeoJSON file/string'), and clearly enumerates what it validates (broken geometry, malformed structure, self-intersections, out-of-range coordinates). It distinguishes itself from sibling tools like fix_geometry and geometry_health_report by framing validation as a pre-use gate.

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?

Clear usage context is provided: use this before trying to use the GeoJSON elsewhere, with example user queries like 'is this GeoJSON valid' and 'lint my GeoJSON'. It does not explicitly name alternatives or exclusions, but the application context is strong enough to guide selection.

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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