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raheb77

io.github.raheb77/saudi-open-data-mcp

by raheb77

query_dataset

Query local canonical records by exact dataset ID and optional exact-match filters. Get matching records without remote fetch, enabling precise dataset lookups.

Instructions

Query local canonical records for an exact dataset_id using exact-match filters only. Local-only; no remote fetch.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoOptional maximum number of matching records to return. Example: 10.
filtersNoOptional exact-match filters keyed by canonical record field. Example: {'currency_code': 'USD'} or {'observation_month': '2026-01'}.
dataset_idYesExact canonical dataset_id from the registry. Examples: 'sama-pos-weekly', 'stats-gov-sa-cpi-headline-monthly'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior2/5

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

With no annotations, the description must carry the full burden of behavioral disclosure. It mentions 'Query' implying a read operation but does not explicitly state read-only or non-destructive behavior. It also does not disclose error handling, pagination, or any rate limits. The phrase 'Local-only' adds some context but is insufficient for a tool that could be called by an agent without knowing side effects.

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 two sentences with no fluff, front-loading the core purpose and then adding the local-only constraint. Every word earns its place, making it highly efficient for an agent to parse quickly.

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 tool has an output schema and the parameters are fully described, the description is mostly complete. It clearly states what the tool does and its constraints. It could mention pagination or behavior when no results are found, but these are not critical for the agent to decide whether to call it. The presence of an output schema reduces the need to describe return values.

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 description coverage is 100%, so the baseline is 3. The description does not add significant meaning beyond the schema; it reiterates 'exact-match filters only' which is already stated in the filters parameter description. It does not clarify the limit parameter behavior or provide additional context for dataset_id beyond the examples already in 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 the tool queries local canonical records for an exact dataset_id using exact-match filters only, which is a specific verb and resource. It also differentiates itself from siblings by emphasizing 'Local-only; no remote fetch' and 'exact-match filters only', which distinguishes it from search_datasets and preview_dataset.

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 provides clear context: it's for querying local records with exact-match filters, and it explicitly states 'Local-only; no remote fetch' which implies it's not for remote data. However, it does not explicitly name alternative tools like search_datasets for fuzzy search or download_dataset for fetching remote data, so it stops short of explicit exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.