CityDPC-MCP

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load_dataset

Loads a GML or JSON dataset for CityJSON/CityGML building inspection, analysis, or editing, creating an original-state snapshot.

Instructions

Lädt ein Dataset aus einer GML- oder JSON-Datei.

Erstellt automatisch einen Snapshot des ursprünglichen Zustands.

Args: filename: Der Name der GML- oder JSON-Datei (z.B. 'EssenExample.gml')

Returns: str: Erfolgsmeldung oder Fehlermeldung

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": {
      "filename": "<filename>"
    },
    "name": "load_dataset"
  }
}

Input Schema

Name	Required	Description	Default
`filename`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 30, 2026

TDQS

B3.3/5.0

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 an important side effect: an automatic snapshot of the original state is created. However, it omits whether loading overwrites an existing dataset, what permissions are needed, and how failures are handled.

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?

Front-loaded with the core action and side effect, then terse Args/Returns. Slightly wasteful that the Returns line restates something the output schema already covers, but the overall length is appropriate.

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 need not be explained. The key side effect (snapshot) is documented, but for a state-changing load operation the description leaves open whether existing data is replaced and what the snapshot implies for rollback_to_snapshot.

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: filename is identified as the GML or JSON file name, with a concrete example ('EssenExample.gml') and the accepted formats. This meaningfully clarifies the single parameter beyond the bare string type.

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+resource: loads a dataset from a GML or JSON file, and notes the auto-snapshot. An agent can distinguish it from create_dataset/save_dataset reasonably well. It stops short of explicitly naming the siblings it differs from, so not 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?

No statement of when to use this versus create_dataset, list_datasets, or save_dataset. Usage is only weakly implied by 'loads from a file'. No prerequisites (file location, source of filename) or exclusions are given.

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