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Hug0x0
by Hug0x0

singapore_query_dataset

Read-onlyIdempotent

Query tabular data.gov.sg datasets using exact filters, column text search, field selection, sorting, and pagination; preserves upstream values and units without SQL.

Instructions

Query any tabular data.gov.sg dataset. Exact filters, column text search, projection, sorting and pagination. Preserve upstream values/units; no SQL. Non-tabular or unsupported datasets can be downloaded instead.

Input Schema

NameRequiredDescriptionDefault
qNoSearch text by column, e.g. {"school_name":"RAFFLES"}
sortNoe.g. month desc, resale_price desc; inspect fields first
limitNo
fieldsNo
offsetNo
filtersNoExact equality by field name, e.g. {"town":"ANG MO KIO"}
dataset_idYes

Output Schema

NameRequiredDescriptionDefault
okYes
dataNo
errorNo
statusNo
sourcesYes
metadataNo
providerNo
warningsNo
served_atYes
error_codeNo
source_urlNo
dataset_urlNo
catalog_metadataNo
catalog_snapshot_atNo
retry_after_secondsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.7/5.0
Behavior3/5

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

The annotations already establish that this is a read-only, idempotent, non-destructive operation. The description adds useful constraints such as preserving upstream values/units and not supporting SQL, but it does not elaborate on rate limits, authentication, or result-shape behavior beyond what the output schema provides.

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, with the core capability stated first. Each sentence adds useful information: query capability, supported operations, semantic constraints, and the non-tabular alternative.

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?

Given the available output schema and annotations, the description is largely complete for a generic query tool. It covers tabular scope, query operations, data-preservation semantics, and the download alternative. It could be stronger by explicitly routing agents to inspect_dataset or specialized query tools before calling this one.

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 description coverage is low at 43%, so the description must compensate. It maps the main parameter groups to behavior: exact filters, column text search, projection, sorting, and pagination, which corresponds to filters, q, fields, sort, limit, and offset. It does not explain every parameter detail, but it adds meaningful semantic coverage beyond the schema.

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 and resource: query any tabular data.gov.sg dataset. It also clarifies the supported operations and notes that non-tabular datasets should use download instead. It does not clearly distinguish generic querying from the many dataset-specific sibling query tools.

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

Usage Guidelines3/5

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

It provides implied guidance: use this for tabular datasets and use download for non-tabular or unsupported datasets. However, it does not explicitly say when to prefer this generic tool over specialized tools like singapore_query_hdb_resale or when to inspect a dataset first.

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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