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CityDPC-MCP

CityDPC-MCP

by HamerCode
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

calculate_roof_volume_by_id

Calculate the roof volume of a specific building using its GML ID and return the result in cubic meters.

Instructions

Berechnet das Dachvolumen für ein spezifisches Gebäude.

Args: building_id: Die gml:id des Gebäudes

Returns: float: Das berechnete Dachvolumen in Kubikmetern

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": "calculate_roof_volume_by_id"
  }
}

Input Schema

NameRequiredDescriptionDefault
building_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden. It discloses the return type and unit (float, cubic meters), but says nothing about dependencies (e.g. whether the building must be loaded first), permissions, or error behavior for a computation 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 docstring is short, front-loaded, and uses a clean Args/Returns structure with no wasted prose. Slightly boilerplate, but efficient.

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 the Returns line is somewhat redundant and return values need not be re-explained. However, with no annotations and a dependency-sensitive computation, the description leaves behavioral gaps that an agent would need to resolve.

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: it clarifies that building_id is "Die gml:id des Gebäudes," adding meaning beyond the bare string type in the schema. Only one parameter, and its semantics are adequately explained.

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?

The description states a specific verb ("Berechnet") and resource ("Dachvolumen") scoped to a single building, which is clearly distinct from retrieval siblings like get_building_by_id. It does not explicitly name alternatives, but the purpose is unambiguous.

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 this tool versus siblings, no prerequisites, and no exclusions. The agent must infer usage purely from the tool name.

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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