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census_demographics

Read-onlyIdempotent

Race, ethnicity, and age breakdown for a US geography. Returns counts for white, black, Asian, AIAN, NHPI, other, two-or-more, plus Hispanic/Latino total and median age. Source: ACS 5-year.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
msaNo5-digit Metropolitan Statistical Area code. Required for msa level.
yearNoACS 5-year endpoint year (default 2023).
zctaNo5-digit ZIP Code Tabulation Area. Required for zcta level.
levelYesGeography level: 'us', 'state', 'county', 'zcta' (ZIP), 'place' (city), 'tract', 'msa'.
placeNoCensus place FIPS (city). Required for place level.
stateNo2-letter state code (e.g. 'TX') or 2-digit FIPS. Required for state/county/place/tract levels.
tractNo6-digit census tract code. Use '*' for all tracts in a county.
countyNo3-digit county FIPS. Use '*' for all counties in a state. Required for county/tract levels.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / year / type
      Previous value: -"number"New value: +"integer"
  2. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, open-world), so the bar is lower. The description adds value beyond them by disclosing the data provenance ('Source: ACS 5-year'), which tells the agent these are 5-year estimates, and by enumerating the returned demographic fields since no output schema exists.

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?

Two tight sentences, front-loaded with the subject/scope statement, followed by the concrete returned fields and the source. No filler or repetition.

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?

With no output schema, the prose enumeration of returned counts and median age is genuinely necessary and present, and the source/vintage is disclosed. The main gap is that the hierarchical geography-level mechanics (which level pairs with which code parameter) are left entirely to the schema.

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 all eight parameters (level, state, county, tract, place, msa, zcta, year) are already documented in the schema, including which are required per level. The description adds no parameter-level detail beyond that, so the baseline 3 is correct.

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 resource (race, ethnicity, age breakdown) and scope (US geography), plus the data source, so an agent can tell it apart from census_population or census_income_housing by subject matter. It does not, however, explicitly name or contrast any sibling tool.

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 when-to-use guidance, no exclusions, and no mention of alternatives such as census_population for total counts or census_geography_lookup for resolving place names. The requirement to supply a geography level is only implicit in the phrase 'for a US geography'.

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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