CDC Social Vulnerability Index

get_county_svi

Get CDC Social Vulnerability Index data for counties in a state.

Returns overall SVI percentile ranking and all four theme breakdowns
(socioeconomic status, household composition/disability, minority
status/language, housing type/transportation) plus key indicator
estimates for each county.

SVI values range 0-1 (percentile ranking); higher = more vulnerable.

Args:
    state: Two-letter US state abbreviation (e.g. 'WA', 'CA').
    county_fips: Optional 5-digit county FIPS code to get a single county.
    year: SVI data year (default 2022, currently only 2022 available).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`year`	No
`state`	Yes
`county_fips`	No

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?

With no annotations provided, the description carries the full burden. It discloses important behavioral traits: returns percentile rankings (0-1) with interpretation ('higher = more vulnerable'), includes all theme breakdowns and indicator estimates, and notes that only 2022 is available. This goes beyond a simple 'Get data' and sets expectations about output semantics and data constraints. It doesn't explicitly mention error handling or that it's a read-only operation, but the nature of the tool is clear.

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 and front-loaded. First sentence states the purpose, second describes what is returned, third explains value interpretation, and a clear Args list follows. Each sentence earns its place by conveying essential information about output, parameter semantics, and constraints without redundancy. It is appropriately sized for a tool with three parameters and no schema descriptions.

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?

The description covers all key aspects: purpose, parameters, return value composition, interpretation, and data-version constraint. An output schema exists, which further specifies the return shape. Given the moderate complexity, the description is complete enough for an agent to correctly select and invoke the tool. It does not explicitly contrast with siblings, but the county-level scope and return details are sufficient to differentiate in most contexts.

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?

The input schema has zero description coverage, so the description fully compensates. It explains each parameter: state format (two-letter abbreviation with examples), county_fips (optional 5-digit code for a single county), and year (default 2022, currently only 2022 available). This adds meaningful semantic detail beyond the bare schema types and defaults.

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?

Description clearly states the tool's purpose: 'Get CDC Social Vulnerability Index data for counties in a state.' It specifies the resource (counties, SVI data), the scope (state, optional single county), and the return content (overall SVI plus four theme breakdowns). This distinguishes it from sibling tools like get_tract_svi (county vs tract) and gives a specific verb and resource.

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?

Provides clear usage context: you pass a state and optionally a county_fips to get a single county, plus year with default 2022. It does not explicitly mention alternatives or when not to use this tool, but the context is sufficiently clear for selecting it over siblings (e.g., county-level vs tract-level). No exclusions are stated, but the description implies the appropriate scope.

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

All CDC Social Vulnerability Index tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0

Disambiguation5/5

Each tool addresses a distinct need: comparing counties, fetching county-level data, ranking most vulnerable, and fetching tract-level data. There is no overlap or ambiguity between them.

Naming Consistency4/5

Most tools follow a 'get_' prefix pattern with descriptive suffixes, but 'compare_svi' deviates by using 'compare' instead of 'get_'. Despite this minor inconsistency, the naming is readable and predictable.

Tool Count5/5

The server has 4 tools, which is well-scoped for its specialized purpose. Each tool covers a distinct query type without unnecessary bloat or overlap.

Completeness4/5

The set covers county and tract-level retrieval, county comparison, and vulnerability ranking. A minor gap is that tract-level data lacks detailed indicator breakdowns, but core workflows are well covered.

Resources

Need Help?