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datasets_numbeo_cities_facets

Aggregate Numbeo city data into country-level counts and refine results with min/max cost-of-living, safety, crime, traffic, pollution, health-care, and quality-of-life filters.

Instructions

Facet the Numbeo cities dataset. Returns terms aggregation counts for the Numbeo cities dataset. Facet enum: country.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over the city name, max 256 characters
facetYesFacet enum: country
countryNoExact country filter, max 128 characters
max_crime_indexNoMaximum Crime Index
min_crime_indexNoMinimum Crime Index
min_safety_indexNoMinimum Safety Index
max_traffic_indexNoMaximum Traffic Index
max_pollution_indexNoMaximum Pollution Index
min_health_care_indexNoMinimum Health Care Index
max_cost_of_living_indexNoMaximum Cost of Living Index (New York = 100)
min_cost_of_living_indexNoMinimum Cost of Living Index (New York = 100)
min_quality_of_life_indexNoMinimum Quality of Life Index

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / facet / enum
      Added value: +[
+  "country"
+]
  2. Addedv1.5.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full burden. It does reveal that the endpoint returns aggregate counts rather than records, but it omits how the many optional filters interact with the aggregation, whether multiple facets are supported, and any limits or pagination behavior.

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?

The description is short and front-loaded, but it has redundancy: 'for the Numbeo cities dataset' appears twice near the top, and 'Facet enum: country' repeats information already in the schema. It could be a single tight sentence without losing information.

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

Completeness2/5

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

For a tool with 12 parameters and no output schema, the description is too thin. It does not specify the shape of the aggregation response, whether the optional filters are applied before faceting, or how missing/empty results behave. An agent would be guessing about the contract.

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%, so the schema already documents all parameters. The description only repeats the facade enum ('country') and adds no extra meaning about filter semantics or how q/country/index filters affect the counts. Baseline 3 is appropriate.

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 ('Facet the Numbeo cities dataset') and clarifies the output type ('terms aggregation counts'), which distinguishes this from search/item siblings. However, it relies on the tool name for differentiation and does not name any sibling explicitly.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus datasets_numbeo_cities_search, datasets_numbeo_cities_item, or the countries facet tool. The only implied usage is 'facet,' which an agent must infer means counts by country rather than retrieving records.

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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