CDC Social Vulnerability Index

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

With no annotations, the description carries the burden of behavioral disclosure. It discloses the year limitation ('currently only 2022 available') and the nature of the output, but does not explicitly state side-effect-free behavior or prerequisites like minimum number of counties. This provides some transparency but leaves gaps.

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 immediately states the action and purpose. The args section is clearly formatted and adds necessary detail 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?

Given the tool's simple comparison nature and the presence of an output schema, the description covers the essential contextual and parameter information. It could mention edge cases like the need for at least two counties, but overall it is adequate.

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?

The description thoroughly explains both parameters beyond the schema: fips_codes format with example, and year default/availability. Since schema coverage is 0%, this compensation is strong.

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 'Compare SVI data across multiple counties' and specifies the output ('side-by-side SVI percentile rankings and key indicators'). This distinguishes it from sibling tools that likely handle single counties or tracts.

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?

It notes the tool is 'Useful for comparing vulnerability across service areas or peer counties,' providing clear context for when to use it. However, it does not explicitly mention alternatives or when not to use it, so it falls short of full guidance.

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

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.

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.

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.

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.

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.

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.

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

With no annotations, the description carries the full burden. It discloses that results are sorted by highest SVI percentile ranking, the default theme, and the limit constraint (default 20, max 100). It does not mention potential edge cases like invalid state or no data, but the core behavior is 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 concise and well-structured, with a clear purpose statement, a use case sentence, and a compact parameter list. Every sentence adds value, and the Args section is easy to scan.

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 3 parameters, an output schema, and a clear use case. The description covers the purpose, parameters, sorting behavior, and context. Given the output schema exists, the description needn't detail return fields, making it contextually complete.

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%, so the description fully compensates. It explains each parameter: state format ('WA', 'CA'), theme options with parenthetical meanings, and limit with default and max. This adds meaning beyond the bare schema types.

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 gets the most vulnerable counties in a state ranked by SVI score. The verb 'Get' and resource 'most vulnerable counties' are specific, and the comparison with sibling tools (compare_svi, get_county_svi, get_tract_svi) is implicit through the focus on ranking counties by vulnerability.

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 explains the tool is useful for identifying priority areas for grants and community health interventions, providing clear context for when to use it. However, it does not explicitly name alternative tools or state when not to use this tool, though the sibling names imply the distinction.

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

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 key behavioral constraints: 'currently only 2022 available,' the default and maximum for limit 'default 50, max 500,' and what data is returned (overall SVI and four themes). It does not mention authentication, rate limits, or pagination, but for a read-only data retrieval tool these are less critical. The explicit year limitation and limit behavior add meaningful context beyond the schema.

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: a one-sentence purpose, one sentence on return value, a use-case sentence, and a compact Args list. Every sentence adds value, and the format is front-loaded with the verb and resource. No redundancy or fluff.

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 that an output schema exists (which covers return structure), the description adequately covers purpose, usage context, parameter semantics, and constraints. It explains what the tool does, what data it returns, when to use it, and all parameter details. The only minor omissions (pagination/error handling) are not critical for a data retrieval tool of this simplicity, and the output schema likely covers return 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?

The schema provides no descriptions (0% coverage), so the description fully compensates. Each parameter is explained with examples: state ('e.g. 'WA', 'CA''), county_fips ('5-digit county FIPS code (e.g. '53033' for King County, WA)'), year ('default 2022, currently only 2022 available'), and limit ('default 50, max 500'). This adds significant meaning not present in 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 clearly states the tool 'Get tract-level CDC Social Vulnerability Index data within a county' and specifies it 'Returns overall SVI and all four theme percentile rankings for each census tract.' This distinguishes it from sibling tools like get_county_svi (county-level) and get_most_vulnerable (ranking) by explicitly focusing on tract-level granularity within a county.

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 identifying sub-county areas of high vulnerability.' It implicitly differentiates from county-level tools by specifying 'tract-level' and 'within a county,' but it does not explicitly name alternative sibling tools or state when not to use this tool. This meets the 'clear context' bar but falls short of explicit exclusions.

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