CDC PLACES Health Data

get_tract_measures

Get all CDC PLACES health measures for a census tract.

Returns tract-level estimates for health outcomes, behaviors, preventive
services, and health status indicators. Tract-level data uses small area
estimation and may have wider confidence intervals than county data.

Args:
    tract_fips: 11-digit census tract FIPS code (e.g. '53033005300').
                Must be a string, not an integer.
    year: Optional release year to filter by. Omit for the most recent data.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`year`	No
`tract_fips`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.6/5.0

Behavior4/5

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

The description discloses the statistical nature of the data (small area estimation, wider confidence intervals), which adds value beyond the basic get operation. However, it does not address error handling, rate limits, or data freshness.

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 well-structured with a concise summary followed by parameter details. It avoids redundancy and front-loads the main purpose.

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

Completeness5/5

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

Given the presence of an output schema (inferred), the description adequately covers the tool's purpose and key caveats. It addresses the main use case and data quality, leaving minimal gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Both parameters are explained with format requirements and default behavior. The tract_fips format is specified with an example and a warning about string vs integer, which adds meaning beyond 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 retrieves CDC PLACES health measures for a specific census tract. It distinguishes from county-level tools by highlighting tract-level estimation, and the sibling set includes get_county_measures, reinforcing differentiation.

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?

While the description implies use for tract-level data, it does not explicitly exclude county-level queries or direct to alternative tools. The mention of wider confidence intervals suggests when to be cautious, but no direct comparison to siblings.

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

All CDC PLACES Health Data tools Inspect

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TDQS

A4.4/5.0

Disambiguation5/5

Each tool serves a distinct purpose: comparing counties, retrieving county measures, getting a single measure across state, tract-level data, and searching measures. No overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (compare, get, get, get, search). Two tools are parallel (get_county_measures, get_tract_measures), and the others are clear and predictable.

Tool Count5/5

Five tools cover the essential operations for a focused health dataset: retrieval at county and tract levels, comparison, state-level query, and metadata search. The count is well-scoped.

Completeness4/5

The set covers main use cases, but lacks a tool to retrieve all measures for all counties in a state at once. Users must either call per county or per measure, which is a minor gap.

Resources

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