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Hug0x0
by Hug0x0

singapore_query_hawker_centres

Read-onlyIdempotent

Query Singapore government hawker centre listings from NEA data using exact filters, column text search, field selection, sorting, and pagination to find specific centres.

Instructions

Query List of Government Markets Hawker Centres (National Environment Agency). Topic: food. Inspect dataset d_68a42f09f350881996d83f9cd73ab02f first for filter fields. Supports exact filters, column text search, fields, sort and pagination. Returns source coverage from the bundled catalog; fetch metadata for current coverage.

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"}

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

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds only a vague note about returning 'source coverage from the bundled catalog' and fetching metadata for current coverage, which hints at data freshness but is unclear. No rate limits, auth, or scope caveats are given.

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?

Three compact sentences with the dataset reference front-loaded and the operational capabilities listed efficiently. The trailing line about catalog coverage is slightly opaque and could be trimmed, but nothing else is wasted.

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?

Because an output schema exists, return values need no explanation, and the prerequisite dataset-inspection step is stated. Still, for a 6-parameter tool with nested objects at 50% schema coverage, more detail on the pagination parameters and field selection would make it fully self-sufficient.

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 50%: q, sort and filters are described in the schema, while limit, offset and fields are not. The description names the same capabilities (exact filters, column text search, fields, sort, pagination), so limit/offset/fields are at least acknowledged, but it adds no syntax or default-limit detail beyond the schema's examples.

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 names a specific verb (query) and resource (Government Markets Hawker Centres, attributed to NEA), plus a topic tag 'food'. It is clearly distinct from generic siblings like singapore_query_dataset. However, it does not explicitly differentiate itself from nearby siblings such as singapore_query_hawker_closures, leaving that inference to the reader.

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 gives one actionable prerequisite: inspect dataset d_68a42f09f350881996d83f9cd73ab02f first for filter fields, and lists supported operations. But it offers no guidance on when to choose this tool over the many other singapore_query_* siblings, nor any exclusions.

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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