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Hk Companies

by io.github.pipeworx-io
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Hk Company Name Changes

hk_company_name_changes
Read-onlyIdempotent

Hong Kong companies that changed their registered name, per the Companies Registry's weekly open-data feed (data.gov.hk, 公司名稱變更, since 2024-12-30). Returns each company's BR number, current English/Chinese name, and the date of the name change, most recent first. The feed has roughly a 1-week publication lag, so a since newer than the latest published week returns zero rows with a data_lag_note explaining it — that is lag, not "no name changes". Use for "Hong Kong companies that changed their name since ", "HK company renames this month".

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "since": "2026-08-17"
    },
    "name": "hk_company_name_changes"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax name changes to return, 1-200 (default 50).
sinceNoOnly include name changes on/after this date (YYYY-MM-DD). Default: 7 days before the latest published week.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses important behavioral details: the data source and publication cadence, the ~1-week lag, zero-row behavior with a data_lag_note, and most-recent-first ordering. This helps the agent correctly interpret empty results and trust the data's freshness.

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 compact and front-loaded, covering purpose, output, ordering, and the critical lag caveat in three sentences. No filler or redundant restatement of the title or schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even without an output schema, the description specifies return fields, ordering, source, lag behavior, and zero-row semantics. The optional parameters are simple and fully described in the schema, so the agent has everything needed to call this tool correctly.

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 100%, so baseline is 3. The description adds meaningful context around the `since` parameter, explaining how the publication lag affects results and what a data_lag_note means, which goes beyond the schema's simple date description.

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 clearly states the tool's function: returning Hong Kong companies that changed their registered name, with specific output fields (BR number, current English/Chinese name, change date). It distinguishes itself from sibling tools like hk_new_companies by focusing on name changes rather than new incorporations.

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

Usage Guidelines4/5

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

The description explicitly provides example use cases such as 'Hong Kong companies that changed their name since <date>' and explains the data lag caveat to prevent misinterpretation. It does not explicitly name alternatives or when not to use this tool, but the context and examples give clear guidance.

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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