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zencity-product

city-data-mcp

Official

Query City Economic Data

query_economics

Fetch economic indicators like unemployment, GDP, CPI, and income for a US, UK, or Canadian city. Auto-detects country or accepts a code for ambiguous city names.

Instructions

Query economic indicators for a city in the US, UK, or Canada.

US: FRED — unemployment, employment, housing price index, per capita income (20 major metros). UK: ONS — CPI, GDP, regional GVA, unemployment. CA: StatCan — CPI, GDP, retail trade (national/provincial).

Country auto-detected or specify with country parameter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYesCity name (e.g., 'Denver', 'Manchester', 'Toronto')
countryNoCountry code. Auto-detected from city name if omitted. Required for ambiguous cities like London, Birmingham, Richmond, Hamilton, Cambridge, Windsor.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations exist, so the description carries the full burden. It usefully discloses the source agencies per country (FRED/ONS/StatCan) and the country auto-detection behavior, but does not cover permissions, rate limits, latency, or what the response contains.

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?

Front-loaded with the core purpose, then compact per-country indicator lists with no wasted prose. The structure makes coverage easy to scan, which is earned length for a multi-region tool.

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?

For a 2-parameter, no-output-schema tool with no annotations, the description supplies the key missing context: which indicators exist in which country and how country resolution works. Return shape is not described, but the indicator enumeration substantially compensates.

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 schema already documents both parameters including examples and the ambiguity caveat for London/Birmingham/Richmond. The description's 'auto-detected or specify' line largely restates the schema's country description, adding no new syntax or format detail. Baseline 3 applies.

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?

States a specific verb (Query) and resource (economic indicators for a city), and enumerates the indicator families per country (FRED unemployment/HPI/income, ONS CPI/GDP/GVA, StatCan CPI/GDP/retail). It distinguishes itself from regional-scoped siblings, though it does not explicitly differentiate from overlapping siblings like query_employment or query_cost_of_living.

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?

Usage is implied rather than stated: the geography lists tell the agent what data this tool covers, and 'Country auto-detected or specify with country parameter' gives one operational rule. There is no explicit when-to-use vs alternatives guidance, nor any exclusion for overlapping siblings such as query_employment or query_housing.

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