SNAP participation, food insecurity indicators, and agricultural statistics
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Average 4.5/5 across 4 of 4 tools scored.
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.
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.
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.
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.
get_agricultural_dataAInspectGet 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 |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 (USDA NASS) and what data is returned, but omits details like authentication needs, rate limits, response size, or error behavior. For a read-only data retrieval tool, this is moderate but not insufficient.
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?
The description is efficiently structured with a two-sentence summary followed by a compact, readable Args list. Every sentence provides useful information, and the most important purpose is front-loaded.
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?
Given a small parameter set (3), an existing output schema, and clear descriptions of return types and use context, the description covers all essential aspects for successful invocation. No critical details are missing.
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?
Schema description coverage is 0%, but the 'Args' block compensates with detailed explanations and examples for each parameter, including default behavior when omitted (e.g., 'Omit to get broad survey', 'Omit to get most recent available data'). This adds substantial meaning beyond the schema.
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?
The description opens with a specific verb+resource: 'Get USDA NASS QuickStats data on agricultural production by state.' It clearly states the return types (crop values, production quantities, farm statistics) and is easily distinguished from sibling tools focused on food environment, food 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?
The description provides clear use context: 'Useful for understanding local food production capacity in grant narratives.' It does not explicitly name alternatives or exclusions for sibling tools, but the context makes when-to-use apparent, so no misleading guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_food_environmentAInspectGet 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 |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and explains the tool's behavior: it 'constructs food access risk profiles' by combining specific ACS variables and outlines the risk logic (limited vehicle access + high poverty). It doesn't disclose rate limits or error behavior, but for a read-only data tool this is reasonably transparent.
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?
The description is compact and front-loaded with the purpose, followed by methodology, use case, and parameter details. Every section earns its place, and the Args section is efficiently structured for agent consumption.
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?
Given the output schema is present, the description doesn't need to detail return values. It covers the tool's purpose, method, parameters, and use case, making it complete for a two-parameter data-access tool.
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?
Schema coverage is 0%, but the comment block fully compensates: it specifies accepted formats for state ('two-letter abbreviation or 2-digit FIPS'), the three-digit format and example for county_fips, and defines the omit behavior for county_fips. This adds substantial meaning beyond the bare schema.
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?
The opening line states a specific verb and resource: 'Get county-level food access risk profiles using Census ACS data.' It further distinguishes the tool by describing its unique method (combining vehicle access, poverty, and SNAP) and food desert risk, separating it from sibling tools like 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?
The description gives clear usage context: 'Useful for identifying areas with barriers to food access in grant applications.' It does not explicitly name alternative tools or when-not-to-use scenarios, but the context is sufficient to infer appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_food_insecurity_indicatorsAInspectGet 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 |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explains the behavioral logic: it combines SNAP rates, poverty rates, and median income, and interprets higher/lower values as correlating with food insecurity. This adds meaningful context beyond a simple data fetch, though it doesn't mention limitations or potential edge cases.
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?
The description is well-structured and front-loaded with the main purpose. Each sentence adds value, and the Args section is separated clearly for readability. It is not overly verbose.
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?
Given the output schema exists (so return format need not be described), this description covers the purpose, usage, and parameter semantics comprehensively. It is complete for a data retrieval tool.
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?
Schema coverage is 0%, so the description fully compensates. The Args block explicitly defines each parameter: state accepts a 2-letter abbreviation or FIPS code, county_fips is a 3-digit code with an example, and year has a default and data range. This is much richer than the bare schema.
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?
The description opens with a specific verb and resource: 'Get food-related economic indicators as proxies for food insecurity by county.' It clearly defines the tool's output (a risk profile) and distinguishes it from sibling tools that focus on agricultural data, food environment, or SNAP participation alone.
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?
The description provides a clear use case: 'Useful for grant narratives demonstrating community need.' It implies when to use it (when assessing food insecurity risk) but does not explicitly mention alternatives or when not to use it relative to the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_snap_participationAInspectGet 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 |
| Name | Required | Description |
|---|---|---|
| result | Yes |
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.
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.
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.
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.
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.
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.
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