CityDPC-MCP

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remove_building_from_dataset

Remove a building from a CityJSON/CityGML dataset using its gml:id. Directly modifies the dataset and reports the remaining building count.

Instructions

Entfernt ein Gebäude aus dem Dataset.

Die Änderung wird direkt im Dataset vorgenommen (Single Source of Truth). Verwende take_snapshot() vor wichtigen Änderungen und save_dataset() zum Speichern.

Args: building_id: Die gml:id des zu entfernenden Gebäudes

Returns: dict: Bestätigungsmeldung mit Anzahl verbleibender Gebäude

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": {
      "building_id": "<building_id>"
    },
    "name": "remove_building_from_dataset"
  }
}

Input Schema

Name	Required	Description	Default
`building_id`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 30, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden and does disclose real behavioral traits: the change is applied directly in-place (Single Source of Truth) and is not persisted without save_dataset(), plus snapshotting enables recovery. It omits permissions/auth needs and whether the removal is reversible by other means, but the persistence and mutation semantics are well conveyed.

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?

Purpose is front-loaded, followed by behavioral context and then Args/Returns. Appropriately sized for a one-parameter mutation tool, though the Returns section is slightly redundant given an output schema exists.

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

Completeness4/5

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

For a single-parameter mutation with no annotations, the description covers purpose, in-place persistence behavior, snapshot/save workflow, and the parameter's meaning. The output schema makes the Returns note unnecessary, and permissions are unaddressed, but nothing critical for correct invocation is missing.

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 description coverage is 0%, so the description must carry the load, and it does explain that building_id is the gml:id of the building to remove. This adds meaningful type/identity semantics beyond the bare 'string' schema, fully covering the single required parameter.

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?

States a specific verb and resource ('Entfernt ein Gebäude aus dem Dataset'), so an agent immediately knows this is a removal operation on a building. It does not, however, distinguish itself from the sibling 'remove_building_attributes', leaving a minor ambiguity between the two removal tools.

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?

Provides explicit workflow guidance: use take_snapshot() before important changes and save_dataset() to persist, naming two sibling tools directly. It stops short of a full when/when-not contrast (e.g., versus remove_building_attributes), but the surrounding context is clear.

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