CityDPC-MCP

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remove_building_attributes

Clears specified attributes from a CityJSON/CityGML building by setting them to None. Provide the building ID and attribute names to update the model.

Instructions

Entfernt Attribute eines Gebäudes (setzt sie auf None).

Args: building_id: Die gml:id des Gebäudes attributes: Liste von Attributnamen (gleiche Namen wie in enrich_building, z.B. "measuredHeight", "roofType"). Unbekannte Namen werden ignoriert.

Returns: dict: Das aktualisierte Gebäude mit allen Attributen

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>",
      "attributes": "<attributes>"
    },
    "name": "remove_building_attributes"
  }
}

Input Schema

Name	Required	Description	Default
`attributes`	Yes
`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

A3.6/5.0

Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose the key trait that removal means setting values to None rather than deleting the keys, plus that unknown names are silently ignored. It does not state whether changes persist to the dataset, whether a save is required, or any permission requirements — significant gaps for a mutation tool.

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 purpose is front-loaded in one sentence, followed by a compact Args/Returns block. Every line adds information; nothing is redundant.

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?

Parameters and return shape are covered, and an output schema exists so return details are a bonus rather than a necessity. The gap is the mutation's lifecycle semantics (persistence, snapshot interaction, auth) which, absent any annotations, leaves the agent guessing about side effects.

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 0%, so the description must compensate, and it does: building_id is identified as the gml:id, and attributes is described as a list of names using the same vocabulary as enrich_building, with concrete examples (measuredHeight, roofType) and the unknown-name fallback.

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: it removes (sets to None) attributes of a building. Referencing enrich_building for attribute naming implicitly signals this is the inverse operation, giving some sibling differentiation, though the contrast is not stated explicitly.

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?

Usage is implied through the link to enrich_building (same attribute names), so an agent can infer this is the undo of enrichment. However, there is no explicit when-to-use or when-not-to-use guidance, nor any mention of prerequisites such as having the building loaded in a dataset.

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