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Opendosm My

by io.github.pipeworx-io
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Opendosm Dataset Meta

opendosm_dataset_meta
Read-onlyIdempotent

Metadata for one Malaysian dataset (data.gov.my): catalogue_id, data_as_of, last_updated, next_update, data_source, update_frequency, plus a sample row showing the available columns. Use this to inspect a series before pulling it, or to check freshness. Requires a dataset id.

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": {
      "id": "cpi_headline"
    },
    "name": "opendosm_dataset_meta"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesDataset id, e.g. "cpi_headline". Required.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "id": "cpi_headline"
+  },
+  {
+    "id": "fuelprice"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. The description adds behavioral context by listing the exact metadata fields and the presence of a sample row, which is beyond the annotations.

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?

Two sentences: first states what is returned, second gives usage guidance. No superfluous text, perfectly sized.

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?

Given the simple tool (1 param, no output schema, rich annotations), the description fully covers its purpose, usage, and output without gaps.

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 100% with clear parameter description. The description mentions 'Requires a dataset id' but does not add new semantic meaning beyond the schema.

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 it returns metadata including specific fields and a sample row, and distinguishes itself from siblings like opendosm_get_dataset and opendosm_list_datasets by focusing on metadata inspection.

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?

Explicitly advises using this tool before pulling data or checking freshness, and notes the requirement of a dataset id. Could mention when not to use, but sufficient for 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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