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Hong Kong Government Development, Geography and Land Information MCP Server

Hong Kong Government Development, Geography and Land Information MCP Server

by hkopenai

Get New Building Plans Processed

get_new_building_plans_processed

Retrieve the number of new building plans processed by Hong Kong's Building Authority for a specified year range, enabling comparison of plan-processing counts over time.

Instructions

Retrieve data on the number of plans processed by the Building Authority in Hong Kong for new buildings within a specified year range.

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": {
      "start_year": "<start_year>",
      "end_year": "<end_year>"
    },
    "name": "get_new_building_plans_processed"
  }
}

Input Schema

NameRequiredDescriptionDefault
end_yearYesEnd year for data range
start_yearYesStart year for data range

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.8

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full behavioral burden. While 'Retrieve' implies a read-only operation, the description does not explicitly state that it is non-destructive, what permissions may be required, or how the data is sourced or refreshed.

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 description is a single, front-loaded sentence with no unnecessary wording. Every part contributes to identifying the data and its scope.

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 simple two-parameter read tool with an output schema, the description gives sufficient context to invoke it correctly: the data subject, authority, and year range are all covered. It could be slightly stronger by clarifying the read-only nature or expected data granularity, but it is largely complete.

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?

The input schema fully documents both start_year and end_year, and the description only restates that a year range is required. It adds no extra meaning, format examples, or constraints beyond what the schema already provides, which is the baseline for 100% coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Retrieve') and precisely names the resource: the number of plans processed by the Building Authority in Hong Kong for new buildings. It clearly scopes the tool to a year range, leaving no ambiguity about what data it returns.

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?

The description says what data is available within a specified year range but gives no explicit guidance on when to use this tool versus other data sources or tools. There is no mention of prerequisites, exclusions, or suitable use cases beyond the implied retrieval context.

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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