CityDPC-MCP

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save_dataset

Save the current version of your CityJSON or CityGML building dataset to a GML/JSON file, keeping all changes. Optionally convert format.

Instructions

Speichert das Dataset dauerhaft in die GML/JSON-Datei.

Übernimmt alle aktuellen Änderungen permanent in die Datei.

Args: description: Beschreibung des Speicherns target_format: Optionales Zielformat ("gml" oder "json"). Wenn None, wird das ursprüngliche Format beibehalten. Returns: dict: Informationen über den Speichervorgang

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "save_dataset"
  }
}

Input Schema

Name	Required	Description	Default
`description`	No
`target_format`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 30, 2026

TDQS

B3.2/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses that saving is permanent ('dauerhaft', 'permanent') and writes all current changes to the file, but says nothing about overwrite behavior, permissions, or reversibility, which matters for a persistence/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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core purpose is front-loaded in the first two sentences with no padding. The Args/Returns blocks re-state structured info, but the overall size remains tight.

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?

An output schema exists, so return values needn't be described. For a 2-parameter persistence tool with no annotations, the description covers purpose and both parameters adequately but omits contrast with snapshot/history siblings and any risk or overwrite 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?

Schema coverage is 0%, so the description must compensate. It explains target_format well, naming the accepted values ('gml' or 'json') and the None default (keep original format), but the 'description' parameter is only glossed as 'Beschreibung des Speicherns', which is thin.

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 (speichern/save) and resource (dataset) plus the destination artifact (GML/JSON file), so the agent knows it persists data. It does not differentiate itself from related state-management siblings like take_snapshot or rollback_to_snapshot, so it stays below a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use save_dataset versus take_snapshot, get_dataset_history, or create_dataset beyond the implicit notion of persisting changes. No prerequisites or exclusions are stated.

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