USDA Food Access

get_snap_participation

Get SNAP/food stamp household participation by county using Census ACS Table B22001.

Returns the number and percentage of households receiving vs not receiving
SNAP/food stamp benefits, broken down by county. Essential for understanding
food assistance utilization in grant target areas.

Args:
    state: Two-letter state abbreviation (e.g. 'WA', 'MS') or 2-digit FIPS code.
    county_fips: Three-digit county FIPS code (e.g. '033' for King County, WA).
                 Omit to get all counties in the state.
    year: ACS 5-year estimate year (default 2022). Data covers year-4 through year.

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

A4.3/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the data source (Census ACS Table B22001), the nature of the output (number and percentage of households), and the optional behavior of county_fips. However, it does not explicitly state that this is a safe read-only operation, mention potential errors, rate limits, or authentication requirements. For a get-style tool, this is adequate but not comprehensive.

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?

The description is well-structured with a summary line, a contextual justification, and a clearly labeled Args section. It is slightly longer than necessary—the 'Essential for understanding' phrase is somewhat promotional—but every sentence adds useful information. The format is scannable and front-loaded with the core 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?

The tool has an output schema, so the description does not need to explain return values in detail. It provides all essential input information, data source context, and behavior for optional parameters. The description is complete for a data retrieval tool with well-documented parameters and an existing output schema.

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?

Schema description coverage is 0%, meaning the input schema provides no descriptions. The description fully compensates by explaining each parameter in detail: state (two-letter abbreviation or FIPS code), county_fips (three-digit code, with an example and behavior when omitted), and year (default 2022, coverage window). This adds significant meaning beyond the bare 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's purpose: getting SNAP/food stamp household participation by county using a specific Census ACS table. It uses a specific verb ('Get') and resource, and it is distinct from sibling tools like get_agricultural_data or get_food_environment. The scope (county-level) and data source are immediately clear.

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?

The description provides clear context on when to use the tool, noting it is 'Essential for understanding food assistance utilization in grant target areas.' It does not explicitly mention when not to use it or name alternatives, but the sibling tools are distinct enough that the context alone guides appropriate use. The parameter guidance on omitting county_fips to get all counties also helps.

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

All USDA Food Access 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

A4.3/5.0

Disambiguation2/5

Three of the four tools (get_food_environment, get_food_insecurity_indicators, get_snap_participation) are highly overlapping, all operating at the county level with ACS data and combining SNAP, poverty, and income indicators. An agent would struggle to distinguish between them, as their purposes and outputs are nearly interchangeable. Only get_agricultural_data is clearly distinct.

Naming Consistency5/5

All tools follow a consistent 'get_<descriptive_noun>' pattern with snake_case. The verbs are uniform and the nouns clearly indicate the data being retrieved. This is a highly predictable naming scheme.

Tool Count5/5

Four tools is a well-scoped count for a specialized data access server. Each tool addresses a distinct aspect of food access data (agricultural production, food environment, food insecurity, SNAP participation), and the count is neither sparse nor overwhelming.

Completeness4/5

The server covers the core data needs for understanding food access: agricultural production, food environment risk, food insecurity indicators, and SNAP participation. Minor gaps exist, such as lack of food price or food store location data, but these are not fundamental to the server's stated purpose and agents can work around them.

Resources

Need Help?