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Choose Geography Columns From a Dataset

identify_dataset_geography

Read-onlyIdempotent

Identify geographic columns in a dataset from bounded samples, then propose country, level, and reference-system bindings for high-confidence results.

Instructions

Multi-column dataset classifier: use when you do not yet know which column or latitude/longitude pair represents geography. Pass bounded samples from several named scalar columns; it selects the plausible geography column(s), then proposes a country, level, and maintained reference-system binding. It does not validate a preselected identifier column value-by-value; after selecting a code column, use identify_reference_system when explicit verification is needed. Returns up to three reviewable bindings and recommends only an unambiguous high-confidence result. No geometry is loaded and no full dataset is retained. No payment required.

Input Schema

TableJSON Schema

Name	Required	Description
`columns`	Yes	Structurally filtered columns with deterministic representative scalar values. Do not pre-label their geographic system.
`request_id`	No
`country_scope`	No	Optional caller-declared ISO3 hint. Omit when unknown.
`dataset_context`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`ok`	No
`error`	No
`limit`	No
`reason`	No
`status`	No
`batch_id`	No
`guidance`	No
`warnings`	No
`next_step`	No
`candidates`	No
`request_id`	No
`column_count`	No
`clarification`	No
`meter_receipt`	No
`review_required`	No
`identifier_count`	No
`coordinate_binding`	No
`settlement_receipt`	No
`recommended_binding`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.0.1Sep 24, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "additionalProperties": true,
+  "anyOf": [
+    {
+      "required": [
+        "ok",
+        "status",
+        "candidates"
+      ]
+    },
+    {
+      "required": [
+        "error"
+      ]
+    }
+  ],
+  "properties": {
+    "batch_id": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "candidates": {
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "clarification": {
+      "type": "object"
+    },
+    "column_count": {
+      "minimum": 0,
+      "type": "integer"
+    },
+    "coordinate_binding": {
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "error": {
+      "additionalProperties": true,
+      "properties": {
+        "code": {
+          "type": "string"
+        },
+        "details": {
+          "type": "object"
+        },
+        "message": {
+          "type": "string"
+        },
+        "retry_hint": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "code",
+        "message"
+      ],
+      "type": "object"
+    },
+    "guidance": {
+      "type": "object"
+    },
+    "identifier_count": {
+      "minimum": 0,
+      "type": "integer"
+    },
+    "limit": {
+      "minimum": 0,
+      "type": "integer"
+    },
+    "meter_receipt": {
+      "type": "object"
+    },
+    "next_step": {
+      "additionalProperties": true,
+      "anyOf": [
+        {
+          "required": [
+            "action"
+          ]
+        },
+        {
+          "required": [
+            "tool"
+          ]
+        }
+      ],
+      "properties": {
+        "action": {
+          "type": "string"
+        },
+        "arguments": {
+          "type": "object"
+        },
+        "tool": {
+          "type": "string"
+        },
+        "url": {
+          "type": "string"
+        }
+      },
+      "type": "object"
+    },
+    "ok": {
+      "type": "boolean"
+    },
+    "reason": {
+      "type": "string"
+    },
+    "recommended_binding": {
+      "type": [
+        "object",
+        "null"
+      ]
+    },
+    "request_id": {
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "review_required": {
+      "type": "boolean"
+    },
+    "settlement_receipt": {
+      "type": "object"
+    },
+    "status": {
+      "type": "string"
+    },
+    "warnings": {
+      "items": {
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "type": "object"
+}

AddedSep 24, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds meaningful context beyond that: it returns up to three reviewable bindings, recommends only unambiguous high-confidence results, loads no geometry, retains no full dataset, and requires no payment. This gives agents a clear picture of operational side effects.

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 dense but every sentence earns its place. The opening phrase frames the tool as a classifier and the primary use case, followed by concrete operational details, exclusions, and output characteristics. No filler or redundant restatement of the title.

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 complexity and the presence of an output schema, the description covers the essential call context: when to use, what to pass, what it returns (up to three bindings, no geometry loaded), and how it relates to a sibling tool. The only not-explicitly-stated edge cases (e.g., ambiguous input) are adequately hinted by 'recommends only an unambiguous high-confidence result.'

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

Parameters4/5

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

Schema coverage is only 50%, so the description compensates by explaining the meaning of the core 'columns' parameter: pass bounded samples from several named scalar columns, and do not pre-label their geographic system. It also clarifies country_scope as an ISO3 hint. However, request_id and dataset_context are not elaborated in the description, leaving some params undocumented.

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 ('selects', 'proposes') and a precise resource ('geography columns from a dataset'). It clearly differentiates from the sibling identify_reference_system by explaining this tool is for discovering geography columns when unknown, not for validating a preselected code column.

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?

Explicitly says 'use when you do not yet know which column or latitude/longitude pair represents geography.' It also names the alternative (identify_reference_system) and the condition for switching (after selecting a code column, when explicit verification is needed). No ambiguity about when to call this tool.

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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