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city-data-mcp

Official

Create Census Peer Cohort (Fast)

create_census_cohort

Find peer cities by comparing Census demographics like population, income, and housing. Quick demographic peer matching for urban analysis.

Instructions

Find peer cities based on Census demographic data. Fast — uses only Census ACS data across ~75 cities.

Compares: population, income, poverty, education, housing costs, commuting patterns, region.

Criteria: "balanced", "size", "economics", "housing", "education", "commuting", "region".

Use this for quick demographic peer matching. For richer multi-source comparison (economics, crime, employment), use create_full_cohort instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityYesTarget city to find peers for (e.g., 'Denver', 'Austin')
criteriaNoWhat dimensions to weight most in finding peersbalanced
cohortSizeNoHow many peer cities to return (default 5)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses that it uses only Census ACS data across about 75 cities and lists comparison dimensions, but it does not state whether it writes or stores anything, what permissions are needed, or what the return format looks like.

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 front-loaded with purpose and speed, then lists compared dimensions and criteria, and ends with the alternative. Every sentence earns its place and there is no waste.

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 simple three-parameter read-style tool with 100% schema coverage and no output schema, the description is nearly complete: it covers purpose, data source, comparison criteria, and the alternative tool. It leaves minor gaps around return shape and side effects, but not enough to prevent correct invocation.

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 city, criteria, and cohortSize. The description lists the criteria enum values and comparison dimensions, adding some context, but does not add syntax or default behavior beyond the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

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 states a specific verb and resource: find peer cities based on Census demographic data. It distinguishes this fast, Census-only tool from the richer multi-source sibling create_full_cohort. An agent can identify its scope and purpose without opening the schema.

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

Usage Guidelines5/5

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

It explicitly says to use this for quick demographic peer matching and directs the agent to create_full_cohort for richer multi-source comparison. The when-to-use and alternative conditions are stated plainly.

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