USDA Food Access
Server Details
SNAP participation, food insecurity indicators, and agricultural statistics
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.3/5 across 4 of 4 tools scored.
The tools have distinct primary purposes: agricultural data, food environment risk profiles, food insecurity indicators, and SNAP participation. However, get_food_environment and get_food_insecurity_indicators both use similar Census data (poverty, SNAP) and could be confused for overlapping risk assessment functions, though their specific focuses differ slightly.
All four tools follow a consistent verb_noun pattern with 'get_' prefix and descriptive nouns (agricultural_data, food_environment, food_insecurity_indicators, snap_participation). The naming is uniform and predictable throughout the set.
Four tools is reasonable for a USDA food access server, providing focused coverage of key data areas. It's slightly lean but covers agricultural production, food access risk, food insecurity proxies, and SNAP participation—core components for grant narratives and analysis.
The tools cover data retrieval well for agricultural stats, food access risks, and SNAP participation, but there are notable gaps: no tools for updating or managing data (expected for a data server), and missing broader food system metrics like retail access or nutrition programs beyond SNAP. The surface is functional but not comprehensive for the food access domain.
Available Tools
4 toolsget_agricultural_dataAInspect
Get USDA NASS QuickStats data on agricultural production by state.
Returns crop values, production quantities, and farm statistics from the
USDA National Agricultural Statistics Service. Useful for understanding
local food production capacity in grant narratives.
Args:
state: Two-letter state abbreviation (e.g. 'WA', 'IA').
commodity: Agricultural commodity to query (e.g. 'CORN', 'SOYBEANS', 'WHEAT',
'CATTLE', 'MILK'). Omit to get a broad survey of crops.
year: Year for data (e.g. 2022). Omit to get the most recent available data.| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| state | Yes | ||
| commodity | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that it returns crop values, production quantities, and farm statistics, but omits details about data freshness, caching, or rate limiting given no annotations exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear paragraph separation between purpose, return values, use case, and parameter definitions, though the Args section is lengthy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequately complete for a simple three-parameter tool, covering data source, return types, and all parameter semantics without needing to detail return values since an output schema exists.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Excellent compensation for 0% schema description coverage by providing detailed examples (e.g., 'WA', 'CORN', 'SOYBEANS') and semantic meaning for all three parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it retrieves USDA NASS QuickStats production data, distinguishing it from sibling tools focused on food environment, insecurity, and SNAP participation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Mentions utility for 'grant narratives' and 'local food production capacity' but lacks explicit guidance on when to choose this over food environment or insecurity tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_food_environmentAInspect
Get county-level food access risk profiles using Census ACS data.
Constructs food access risk profiles by combining vehicle access (B25044),
poverty status (B17001), and SNAP participation (B22001). Limited vehicle
access combined with high poverty indicates food desert risk. Useful for
identifying areas with barriers to food access in grant applications.
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.| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | ||
| county_fips | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden well by disclosing data sources, the compositional methodology, and risk calculation logic, though operational traits (caching, rate limits) are absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with front-loaded purpose, followed by methodology, use case, and Args section; every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complete for a data retrieval tool with output schema present; covers data sources, risk interpretation logic, and all input parameters sufficiently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Excellent compensation for 0% schema description coverage by providing detailed semantics, formats, and examples for both parameters (state abbreviations/FIPS, county FIPS codes, and omission behavior).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it retrieves county-level food access risk profiles using specific Census ACS variables (B25044, B17001, B22001) and distinguishes from siblings by emphasizing the composite 'food desert risk' construction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly identifies the use case ('grant applications') and logic ('Limited vehicle access combined with high poverty indicates food desert risk'), though it doesn't explicitly contrast with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_food_insecurity_indicatorsAInspect
Get food-related economic indicators as proxies for food insecurity by county.
Combines SNAP participation rate (B22001), poverty rate (B17001), and
median household income (B19013) to build a food insecurity risk profile.
Higher SNAP rates, higher poverty, and lower income correlate with greater
food insecurity. Useful for grant narratives demonstrating community need.
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.| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| state | Yes | ||
| county_fips | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description carries full burden by disclosing specific Census variables used (B22001, B17001, B19013), temporal coverage ('year-4 through year'), and correlation logic.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with purpose first, then methodology, use case, and Args; every sentence provides distinct value though Args formatting consumes vertical space.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Comprehensive for a data retrieval tool: specifies data sources (Census variables), geographic granularity (county), temporal aspects (ACS 5-year), and use case; appropriately relies on output schema for return value details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, Args section fully compensates with precise formats, examples ('WA', '033'), and default value explanations for all three parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb+resource ('Get food-related economic indicators'), explicitly defines composite methodology (SNAP+poverty+income) that distinguishes it from single-indicator sibling get_snap_participation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides use case ('grant narratives demonstrating community need') but lacks explicit when/when-not guidance or comparison to siblings get_food_environment or get_agricultural_data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_snap_participationAInspect
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.| Name | Required | Description | Default |
|---|---|---|---|
| year | No | ||
| state | Yes | ||
| county_fips | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses ACS 5-year temporal coverage ('year-4 through year'), explains returned metrics (counts vs percentages), and cites data provenance despite no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with front-loaded purpose statement followed by Args block; every sentence provides distinct value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequately covers data source, temporal scope, and return value summary; defers detailed output structure to existing output schema appropriately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Comprehensive Args section compensates for 0% schema description coverage with clear semantics and examples for state (WA/MS), county_fips (033), and year parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb+resource (Get SNAP participation), cites exact Census table B22001, clearly distinguishes from siblings via focus on specific program participation data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides use case context ('grant target areas') but lacks explicit guidance on when to use vs siblings like get_food_insecurity_indicators.
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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