EPA Air Quality System (AQS) MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose based on the combination of data type (annual/daily/quarterly summary, sample data, monitors, lists) and geographic scope (box, CBSA, county, site, state). The descriptions explicitly differentiate them, making it easy for an agent to select the correct tool without ambiguity. For example, aqs_annual_summary_by_box vs. aqs_annual_summary_by_county serve the same data type but for different geographic boundaries.
Naming Consistency5/5
All tool names follow a highly consistent pattern: 'aqs_' prefix, then data type (e.g., annual_summary, daily_summary, list, monitors, sample_data), then 'by_' and the geographic scope (box, cbsa, county, site, state). Minor exceptions like aqs_is_available and aqs_signup are still clear and fit the overall naming scheme, maintaining perfect consistency across the set.
Tool Count2/5
With 31 tools, the count is excessive for the server's purpose of querying EPA air quality data. Many tools are redundant variations (e.g., five different geographic scopes for each data type like annual_summary, daily_summary, sample_data), creating a bloated interface that could overwhelm agents. A more streamlined design with fewer, more flexible tools would be more appropriate.
Completeness5/5
The tool set provides comprehensive coverage for the EPA AQS domain, including data retrieval (summaries, sample data, monitors), metadata listing (states, counties, sites, parameters, CBSAs), and operational support (availability check, signup). It supports all key geographic scopes and data aggregation levels, leaving no obvious gaps for typical air quality analysis workflows.
Average 3.9/5 across 31 of 31 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what data is returned (daily summaries with arithmetic mean, maximum values, etc.) but doesn't mention important behavioral aspects like rate limits, authentication requirements (though these are covered in parameter descriptions), error handling, pagination, or data freshness. For a tool with 9 parameters and no annotation coverage, this is insufficient.
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 appropriately concise with two sentences that efficiently convey the core functionality. The first sentence states the purpose and what's included in the summary, while the second specifies the bounding box mechanism. There's no wasted language, though it could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, no output schema, no annotations), the description is incomplete. It doesn't explain the return format, error conditions, rate limits, or how results are structured. For a data retrieval tool with multiple parameters and no output schema, users need more guidance about what to expect from the response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema - it mentions 'Specify the bounding box using minimum and maximum latitude/longitude coordinates' which is already clear from parameter names and descriptions. No additional parameter semantics are provided in the description itself.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get daily summary air quality data for all monitoring sites within a geographic bounding box' with specific details about what the summary includes. It distinguishes from some siblings by specifying 'daily' and 'by box' but doesn't explicitly differentiate from other daily summary tools (e.g., aqs_daily_summary_by_cbsa, aqs_daily_summary_by_county).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the phrase 'within a geographic bounding box' and mentions specific coordinate parameters, suggesting this tool is for geographic area queries. However, it doesn't explicitly state when to use this tool versus alternatives like aqs_daily_summary_by_cbsa or aqs_daily_summary_by_county, nor does it mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It describes what data is returned but lacks critical behavioral details: authentication requirements (implied by email/key parameters but not stated), rate limits, error handling, or data format. For a tool with 7 parameters and no annotations, this is a significant gap in transparency.
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 efficiently structured in two sentences: the first states the purpose and scope, the second details the statistical content. It's front-loaded with key information and avoids redundancy, though it could be slightly more concise by integrating the statistical list more smoothly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters with full schema coverage but no annotations or output schema, the description adequately covers what data is retrieved but lacks completeness. It doesn't address authentication, error cases, or return format, which are crucial for a data-fetching tool. It's minimally viable but has clear gaps in behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no parameter-specific information beyond implying 'county' scope, which is already clear from the tool name and schema. Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't enhance parameter understanding.
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 verb 'Get' and resource 'annual summary data for all monitoring sites in a county', specifying the geographic scope. It distinguishes from siblings by explicitly mentioning 'by county' and listing detailed statistical content (arithmetic mean, standard deviation, etc.), making the purpose specific and differentiated from other annual_summary_by_* tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like aqs_annual_summary_by_site or aqs_daily_summary_by_county. It mentions the data includes 'all monitoring sites in a county' but does not clarify use cases, prerequisites, or exclusions compared to sibling tools, leaving the agent without contextual decision-making help.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes what data is returned (e.g., statistics, metrics) but lacks critical behavioral details such as authentication requirements (implied by email/key parameters but not stated), rate limits, error handling, or data format. This is a significant gap for a tool with 9 parameters and no output schema.
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 efficiently structured in two sentences: the first states the purpose and scope, and the second details the included data. Every sentence adds essential information without redundancy, making it front-loaded and appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, no annotations, no output schema), the description is incomplete. It lacks information on authentication behavior, response format, error conditions, and how it differs from siblings beyond geographic scope. Without annotations or output schema, the description should provide more behavioral context to guide the agent effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'latitude/longitude coordinates' and 'annual summaries include yearly statistics', but does not provide additional syntax, constraints, or examples not covered in the schema. Baseline 3 is appropriate given high schema coverage.
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 specific action ('Get annual summary data') and resource ('for all monitoring sites within a geographic bounding box'), distinguishing it from siblings by specifying the geographic scope (bounding box) and temporal scope (annual summaries). It explicitly mentions what data is included, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through context (e.g., 'annual summary data' vs. 'daily summary' in sibling tools), but does not explicitly state when to use this tool versus alternatives like aqs_annual_summary_by_county or aqs_daily_summary_by_box. No exclusions or prerequisites are mentioned, leaving some ambiguity for the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes what data is returned (annual summaries with statistics) but lacks behavioral details such as authentication requirements (implied by email/key parameters but not stated), rate limits, error handling, or data format. The description doesn't contradict annotations, but provides minimal operational context beyond the data content.
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 a single, well-structured sentence that efficiently conveys the tool's purpose and data details without unnecessary words. It is front-loaded with the main action and resource, followed by specific data elements. However, it could be slightly more concise by integrating usage context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 parameters, no output schema, no annotations), the description is adequate but incomplete. It explains what data is retrieved but lacks details on authentication, error handling, or output structure. With 100% schema coverage, the parameter documentation is handled, but behavioral aspects are underspecified for a tool with multiple required inputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 8 parameters. The description adds no parameter-specific information beyond what's in the schema, but it does imply the context of 'annual' data and site-specific focus, which aligns with parameters like bdate/edate and state/county/site. Baseline 3 is appropriate as the schema does the heavy lifting.
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 specific action ('Get annual summary data') and resource ('EPA air quality monitoring site'), and distinguishes from siblings by specifying 'by site' in the name and focusing on site-level data. It provides detailed content of the summary (statistics, metrics, exceedance counts) that differentiates it from other summary tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying 'for a specific EPA air quality monitoring site' and listing the type of data returned, but does not explicitly state when to use this tool versus alternatives like aqs_annual_summary_by_county or aqs_daily_summary_by_site. No explicit exclusions or prerequisites are mentioned, though the input schema hints at requirements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 describes the data returned but does not disclose behavioral traits such as authentication requirements (implied by email/key parameters but not stated), rate limits, error handling, pagination, or data format. This is a significant gap for a tool with multiple parameters and no annotation coverage.
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 a single, well-structured sentence that efficiently lists the key data elements (statistics, metrics, exceedance counts) without unnecessary details. It is front-loaded with the main purpose, though it could be slightly more concise by avoiding minor redundancy in listing statistics.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, no output schema, no annotations), the description is moderately complete. It covers the purpose and data scope but lacks details on authentication, rate limits, error handling, and output format. Without annotations or output schema, more behavioral context would improve completeness for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description does not add any parameter-specific details beyond what the schema provides (e.g., it mentions pollutants and statistics but not how they map to parameters). Baseline 3 is appropriate when the schema does the heavy lifting.
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 verb ('Get') and resource ('annual summary data for all monitoring sites in a state'), with specific details about the data included (yearly statistics, metrics, exceedance counts). It distinguishes from siblings by specifying 'by state' scope, unlike other tools that target boxes, CBSAs, counties, or sites.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for annual summary data at the state level, but does not explicitly state when to use this tool versus alternatives (e.g., daily_summary_by_state, quarterly_summary_by_state, or other geographic scopes). It provides context but lacks explicit guidance on exclusions or comparisons with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses authentication behavior (email/API key usage with environment variable fallback) and date constraints (same calendar year requirement), which are useful behavioral traits. However, it doesn't mention rate limits, pagination, error handling, or what 'detailed information' specifically includes beyond the listed fields. It adds some context but leaves gaps for a tool with 7 parameters.
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 clear purpose statement followed by parameter details and authentication note. It's appropriately sized for a 7-parameter tool. Some redundancy exists between description and schema (e.g., date format mentioned in both), but overall it's efficient and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 7 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It covers authentication, parameter semantics, and basic constraints, but lacks details on return format (beyond 'detailed information'), error cases, or performance characteristics. Given the complexity and lack of structured metadata, it should do more to compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds significant value by providing common parameter code examples (e.g., 44201 for Ozone) and clarifying FIPS code formats with specific examples (California='06', Los Angeles='037'). This enhances understanding beyond the schema's generic descriptions. However, it doesn't explain all parameters equally (email/key get less detail).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get all air quality monitors in a county' with specific resource (air quality monitors) and geographic scope (county). It distinguishes from siblings like aqs_monitors_by_state or aqs_monitors_by_site by specifying county-level retrieval, though it doesn't explicitly contrast with all monitor-related siblings. The purpose is specific but could be more explicit about sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through parameter explanations (e.g., date constraints, FIPS codes) but doesn't explicitly state when to use this tool versus alternatives like aqs_monitors_by_state or aqs_monitors_by_site. The sibling list shows many similar tools, but no guidance is provided on selecting among them. Usage is implied rather than explicitly guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It mentions authentication requirements ('Requires state FIPS code and county FIPS code'), which is useful, but lacks details on rate limits, error handling, or response format. It doesn't contradict annotations, but could provide more behavioral context for a tool with 7 parameters.
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 appropriately sized with two sentences: one explaining the purpose and content, and another stating requirements. It's front-loaded with key information, though it could be slightly more structured (e.g., separating authentication from data requirements).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and basic requirements but lacks details on output format, error cases, or usage nuances. Without annotations or output schema, more context would be helpful for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by mentioning state and county FIPS codes, but doesn't provide additional semantics beyond what's in the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
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 verb ('Get'), resource ('daily summary air quality data'), and scope ('for all monitoring sites in a county'), with specific details about what the summaries include. It distinguishes from siblings like aqs_daily_summary_by_site or aqs_daily_summary_by_state by specifying the county-level aggregation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning 'Requires state FIPS code and county FIPS code,' which hints at prerequisites but doesn't explicitly state when to use this tool versus alternatives like aqs_daily_summary_by_site or aqs_annual_summary_by_county. No explicit exclusions or comparisons to siblings are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool retrieves data and aggregates it by quarter, but does not disclose critical behavioral traits such as authentication requirements (though hinted in the schema), rate limits, error handling, or data format. For a data retrieval tool with no annotation coverage, this is a significant gap in transparency.
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 appropriately sized and front-loaded, with the first sentence stating the core purpose. The second sentence elaborates on aggregation details, and the third provides usage context. Every sentence earns its place, though it could be slightly more concise by integrating the usage context into the first sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and usage context but lacks details on authentication, rate limits, error handling, and output format. With no output schema, the description should ideally hint at return values, but it does not, leaving gaps for a tool with multiple required parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description adds no parameter-specific information beyond what the schema provides, such as explaining the relationships between parameters or providing examples. Baseline 3 is appropriate when the schema does the heavy lifting, but no extra value is added.
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 specific action ('Retrieve quarterly summary data'), resource ('air quality monitoring site'), and scope ('aggregate measurements by calendar quarter'). It distinguishes from siblings by specifying quarterly vs. annual/daily summaries and by-site vs. by-county/state/box/cbsa aggregation, which is evident from the sibling tool names.
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 for when to use this tool ('Useful for analyzing seasonal patterns and trends at individual monitoring locations'), which implicitly differentiates it from daily or annual summaries. However, it does not explicitly state when not to use it or name specific alternatives, such as which sibling tools to use for non-quarterly or non-site-specific queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 describes the tool's purpose and output format (CBSA codes with examples), but lacks critical behavioral details such as whether this is a read-only operation, if it requires authentication (though hinted in schema), rate limits, pagination, or error handling.
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 front-loaded with the core purpose, followed by explanatory context and practical examples. Every sentence adds value: defines CBSAs, states usage, and provides concrete examples, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with no output schema and no annotations, the description covers the purpose and usage well but lacks details on output format beyond examples, authentication behavior, or error cases. It's adequate but has clear gaps given the context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the two optional parameters (email and key). The description adds no parameter-specific information beyond what's in the schema, but the baseline is 3 since the schema does the heavy lifting.
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 verb ('Get') and resource ('list of Core Based Statistical Areas with their codes'), specifies what CBSAs are, and distinguishes this tool from siblings by focusing on listing CBSAs rather than performing air quality queries or listing other entities like states or counties.
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 for when to use this tool ('Use CBSA codes for regional air quality queries') and implies it's a prerequisite for CBSA-based queries, but it doesn't explicitly state when not to use it or name specific alternatives among the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the tool's behavior by describing what data is returned (annual statistics, completeness metrics, exceedance counts) and the geographic scope (CBSA representing metropolitan/micropolitan areas). However, it lacks information about authentication requirements (implied by email/key parameters but not stated), rate limits, error conditions, or response format details.
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 appropriately sized and front-loaded with the core purpose in the first sentence. The second sentence provides valuable detail about what's included in the summary. However, the second sentence is quite dense with multiple statistical terms listed, which could potentially be streamlined.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a data retrieval tool with 6 parameters (4 required), 100% schema coverage, and no output schema, the description provides adequate context about what data is returned and the geographic scope. However, it lacks information about authentication (though implied by parameters), rate limits, pagination, or error handling. The absence of annotations means the description should do more to compensate, but it only partially addresses behavioral aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema. It mentions 'parameter code for the pollutant' generally but doesn't provide additional context about the param field or other parameters. Baseline 3 is appropriate when schema does the heavy lifting.
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 verb ('Get') and resource ('annual summary data for all monitoring sites within a Core Based Statistical Area'), and distinguishes from siblings by specifying the geographic scope (CBSA) and temporal granularity (annual vs. daily/quarterly). It provides specific details about what the summary includes (statistics, metrics, exceedance counts).
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 implicitly indicates when to use this tool by specifying 'annual summary data' and 'within a Core Based Statistical Area', which differentiates it from daily/quarterly summaries and other geographic scopes (box, county, site, state). However, it does not explicitly state when NOT to use it or name specific alternatives among the many sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the data returned (daily summaries with specific metrics) and mentions CBSA definition source, but does not cover authentication requirements (implied by email/key parameters but not stated), rate limits, error handling, or response format. It adds some context but leaves significant behavioral aspects unspecified.
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 appropriately sized with three sentences: first states purpose and scope, second defines CBSA, third details included data. Each sentence adds value without redundancy. It could be slightly more front-loaded by integrating the CBSA definition into the first sentence, but overall it's efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (6 parameters, no output schema, no annotations), the description is adequate but has gaps. It covers purpose and data content well, but lacks information on authentication, error handling, and response structure. Without annotations or output schema, the agent may struggle with invocation details beyond basic parameter usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description does not add any parameter-specific information beyond what the schema provides (e.g., it doesn't explain param codes further or date constraints). Baseline 3 is appropriate as the schema does the heavy lifting, but the description adds no extra parameter semantics.
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 verb 'Get' and resource 'daily summary air quality data', specifies the scope 'for all monitoring sites in a Core Based Statistical Area (CBSA)', and distinguishes from siblings by focusing on CBSA geography and daily (not annual/quarterly) summaries. It explains what CBSAs are and what data is included (arithmetic mean, maximum values, etc.).
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 implies usage context by specifying 'daily summary' and CBSA focus, which helps differentiate from annual/quarterly or other geographic tools in the sibling list. However, it does not explicitly state when to use this tool versus alternatives like aqs_daily_summary_by_county or aqs_daily_summary_by_state, nor does it mention prerequisites or exclusions beyond what's in the schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 authentication behavior (email/API key usage with environment variable fallback) and date constraints (same calendar year), which are useful beyond the schema. However, it lacks details on rate limits, error handling, or response format, leaving behavioral gaps for a tool with 6 parameters.
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 clear purpose statement followed by parameter details and a note on authentication. It avoids redundancy and is appropriately sized, though the parameter examples list could be slightly condensed. Every sentence adds value, and it is front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and 6 parameters, the description is moderately complete. It covers authentication, parameter semantics, and constraints, but lacks output details (e.g., response structure) and does not fully address behavioral aspects like error cases. For a data retrieval tool with multiple parameters, more context would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds significant value by providing common parameter code examples (e.g., 44201 for Ozone) and clarifying date format requirements (YYYYMMDD, same calendar year), which enhances understanding beyond the schema's basic descriptions. It does not fully explain all parameters but compensates well for key ones.
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 specific action ('Get all air quality monitors') and resource ('in a state'), distinguishing it from sibling tools like 'aqs_monitors_by_box' or 'aqs_monitors_by_cbsa'. It also specifies the return content ('detailed information about monitors including location, operational dates, and measurement parameters'), making the purpose explicit and distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage through the tool name and parameter context (e.g., state-based filtering), but does not explicitly state when to use this tool versus alternatives like 'aqs_monitors_by_county' or 'aqs_monitors_by_site'. No exclusions or prerequisites are mentioned beyond parameter constraints, leaving usage context partially inferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses aggregation behavior ('aggregate measurements by calendar quarter') and output metrics ('observation counts, arithmetic means, and maximum values'), which is helpful. However, it doesn't mention authentication requirements (implied by email/key parameters), rate limits, or response format details.
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?
Three sentences with zero waste: first states purpose and scope, second explains aggregation methodology, third provides usage context. Front-loaded with core functionality, appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a data retrieval tool with rich schema coverage (100%) but no annotations or output schema, the description provides good context about aggregation methodology and use case. It could be more complete by mentioning authentication implications or typical response structure, but covers the essential behavioral aspects well.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 7 parameters. The description adds no parameter-specific information beyond what's in the schema. The baseline score of 3 reflects adequate coverage when the schema does the heavy lifting.
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 specific action ('Retrieve quarterly summary data'), resource ('air quality monitoring sites in a county'), and scope ('all sites'). It distinguishes from siblings by specifying 'quarterly' aggregation (vs. annual/daily) and 'by county' geography (vs. by site/state/box/cbsa).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('Useful for comparing air quality across multiple monitoring sites within a county') but doesn't explicitly state when to use this tool versus alternatives like aqs_quarterly_summary_by_site or aqs_quarterly_summary_by_state. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool returns (county list with FIPS codes) and implies it's a read operation, but doesn't mention authentication requirements, rate limits, error conditions, or pagination behavior. The description adds basic context but lacks comprehensive behavioral details needed for a tool with authentication parameters.
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 perfectly concise with two sentences that each serve distinct purposes: the first states the tool's function and output format, the second provides usage guidance with concrete examples. There's zero wasted language, and the information is front-loaded with the core purpose stated immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters (including authentication parameters) and no annotations or output schema, the description is adequate but incomplete. It explains the tool's purpose and basic usage but doesn't cover authentication behavior, error handling, or return format details beyond FIPS code examples. Given the complexity of API tools with authentication, more behavioral context would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions the output format (county names with 3-digit FIPS codes) but doesn't provide additional context about the 'state' parameter format or the optional authentication parameters.
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 with specific verbs ('Get a list of counties') and resources ('within a state with their 3-digit FIPS codes'). It distinguishes from siblings by focusing on county listing rather than summary data or other entity types, and provides concrete examples (Los Angeles County, CA = '037') to illustrate the output format.
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 explicitly states when to use this tool: 'Use this to look up county codes for county-level AQS API queries.' This provides clear context for its primary use case. However, it doesn't specify when NOT to use it or mention alternatives among the many sibling tools, which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 that authentication is required ('Requires both state and county codes'), but does not mention rate limits, error handling, or what the return format looks like (e.g., list structure). It adds some context but lacks comprehensive behavioral details for a tool with no annotation coverage.
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 front-loaded with the core purpose, followed by usage guidance and prerequisites in just two sentences. Every sentence adds value without redundancy, making it efficiently structured and appropriately concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description adequately covers the purpose and basic usage but lacks details on return values, error conditions, or advanced behavioral traits. It is complete enough for a simple lookup tool but could be more comprehensive to fully compensate for the missing structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by implying that state and county codes are required for the query, but does not provide additional semantics beyond what the schema specifies. This meets the baseline for high schema coverage.
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 specific action ('Get a list of air quality monitoring sites') and resource ('within a county'), including the output format ('with their 4-digit site codes'). It distinguishes from siblings by focusing on site listing rather than summary data or other list operations, making the purpose explicit and differentiated.
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 for when to use this tool ('Use this to look up site codes for site-level AQS API queries'), which helps guide the agent. However, it does not explicitly state when not to use it or name specific alternatives among the many sibling tools, missing full comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the aggregation method (quarterly summaries) and typical use cases, but doesn't mention authentication requirements (though the schema covers this), rate limits, error conditions, or what the output format looks like. For a data retrieval tool with no annotations, this provides basic behavioral context but lacks operational details.
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 perfectly structured with three sentences that each earn their place: first states the core functionality, second explains the aggregation method and statistics, third provides usage context. It's front-loaded with the main purpose and contains zero wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (6 parameters, no output schema, no annotations), the description provides adequate context for understanding what the tool does and when to use it. However, it doesn't explain what the return data looks like (structure, format, units) or address potential limitations (data availability, processing time). For a data retrieval tool without output schema, more detail about the response would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema already documents all 6 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions aggregation by calendar quarter, which relates to the date parameters, but doesn't provide additional syntax or format details. The baseline of 3 is appropriate when the schema does the heavy lifting.
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 with specific verbs ('retrieve quarterly summary data') and resources ('air quality monitoring sites in a state'), distinguishing it from siblings by specifying quarterly aggregation and statewide scope. It explicitly mentions aggregation by calendar quarter and the types of statistics provided (observation counts, arithmetic means, maximum values).
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 for when to use this tool ('useful for statewide air quality analysis and comparing trends across different regions'), which helps differentiate it from county/site-level quarterly tools. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools (e.g., annual_summary_by_state for yearly data).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 that authentication (email and key) is required but can be set via environment variables, which is useful context. However, it does not mention rate limits, pagination, error handling, or the format of returned data (e.g., JSON structure). The description adds some behavioral context but lacks details on operational traits like performance or output specifics.
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 front-loaded with the core purpose in the first sentence, followed by details on data included and requirements. It uses three concise sentences with zero waste, efficiently covering key aspects without redundancy. Each sentence earns its place by adding necessary context or constraints.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (8 parameters, no annotations, no output schema), the description is reasonably complete. It explains what the tool does, what data it returns, and key requirements. However, without an output schema, it does not describe the return format (e.g., JSON structure, fields), which could be a gap for an agent invoking the tool. The high schema coverage mitigates some of this, but more output context would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description adds minimal value beyond the schema by mentioning the requirement for state, county, and site FIPS codes, but does not provide additional syntax, examples, or constraints not already in the schema descriptions. Baseline score of 3 is appropriate as the schema does the heavy lifting.
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 verb 'Get' and the resource 'daily summary air quality data for a specific monitoring site', specifying the content (arithmetic mean, maximum values, observation counts, AQI values). It distinguishes from siblings by focusing on 'daily summary' and 'by site' rather than annual/quarterly summaries or other geographic aggregations like by box, CBSA, county, or state.
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 for when to use this tool: for daily summary data at a specific site. It implies alternatives by mentioning 'daily summary' (vs. annual, quarterly, sample data) and 'by site' (vs. by box, CBSA, county, state), but does not explicitly name when-not scenarios or specific sibling tools. The requirement for FIPS codes and site number helps identify the correct use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes what the tool returns (classification groups) and their purpose, but doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or response format. The description is accurate but lacks operational details.
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 efficiently structured in two sentences: first stating the purpose with examples, second explaining usage context with concrete examples. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with 2 optional parameters and no output schema, the description provides good context about what's returned and how to use it. It could be more complete by mentioning authentication requirements or response format, but covers the essential purpose and usage well given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters (email and key) with their optional nature and environment variable alternatives. The description adds no parameter-specific information beyond what's in the schema, meeting the baseline for high schema coverage.
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 verb 'Get' and resource 'list of parameter classification groups' with specific examples like 'CRITERIA', 'AIR TOXICS', 'METEOROLOGICAL'. It distinguishes from sibling tools by mentioning aqs_list_parameters as a related but different tool for querying within classes.
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 by explaining that the output class names should be used with aqs_list_parameters for further queries, and gives examples of common classes. However, it doesn't explicitly state when NOT to use this tool or compare it to all sibling alternatives beyond aqs_list_parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions authentication details ('Email and API key can be provided or will use AQS_EMAIL/AQS_API_KEY environment variables'), which is valuable context. However, it doesn't describe the return format (e.g., what data fields are included), pagination behavior, rate limits, or error conditions. For a read operation with 9 parameters and no annotations, this leaves significant gaps in understanding how the tool behaves.
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 and appropriately sized. It starts with the core purpose, provides a usage note, details parameters with examples, includes a geographic example, and ends with authentication info. Most sentences earn their place, though the parameter section is lengthy (but necessary given the complexity). It could be slightly more front-loaded by moving the authentication note earlier, but overall it's efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, no annotations, no output schema), the description is adequate but has clear gaps. It covers authentication and parameter semantics well, but lacks details on the return format (what data the monitors include), pagination, rate limits, or error handling. For a tool that likely returns structured data, the absence of output schema means the description should do more to explain what to expect, leaving the agent with incomplete context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters. The description adds substantial value beyond the schema: it provides common AQS parameter codes with pollutant names (e.g., '44201: Ozone'), clarifies date format constraints ('must be same calendar year'), gives examples of bounding box values, and includes a real-world example for Los Angeles. This compensates well for the schema's technical descriptions, though it doesn't cover all 9 parameters equally (e.g., email/key are only briefly mentioned).
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: 'Get all air quality monitors within a latitude/longitude bounding box.' It specifies the verb ('Get'), resource ('air quality monitors'), and geographic scope ('bounding box'), distinguishing it from sibling tools like aqs_monitors_by_cbsa or aqs_monitors_by_county that use different geographic units. The phrase 'Useful for querying monitors in a geographic region that may span multiple states or counties' further clarifies its unique use case.
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 for when to use this tool: 'Useful for querying monitors in a geographic region that may span multiple states or counties.' This implicitly suggests it's for cross-boundary queries, but it doesn't explicitly state when not to use it or name specific alternatives among the many sibling tools (e.g., aqs_monitors_by_state for single-state queries). The guidance is helpful but lacks explicit exclusions or named comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It adequately describes the tool's function (list retrieval) and purpose (code lookup), but doesn't mention important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or what format the list returns in. The description doesn't contradict any annotations since none exist.
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 perfectly front-loaded with the core purpose in the first sentence, followed by usage guidance and concrete examples. Every sentence adds value without redundancy, making it highly efficient for an AI agent to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list retrieval tool with 100% schema coverage but no annotations or output schema, the description provides adequate context about what the tool does and when to use it. However, it lacks details about the return format (e.g., JSON structure, whether it's paginated) and doesn't mention authentication behavior despite the parameters suggesting it's needed, leaving some gaps in operational understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already fully documents both parameters (email and key) with their optional nature and environment variable alternatives. The description doesn't add any parameter-specific information beyond what's in the schema, meeting the baseline expectation when schema coverage is complete.
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 specific action ('Get a list'), resource ('all US states'), and output format ('with their 2-digit FIPS codes'). It distinguishes itself from sibling tools by focusing on state listing rather than summary or monitoring operations, and provides concrete examples (California='06') to reinforce understanding.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Use this to look up state codes for other AQS API queries.' This provides clear context about its purpose as a reference tool for subsequent operations, distinguishing it from data retrieval siblings like aqs_annual_summary_by_state.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing authentication behavior (environment variable fallback), date constraints (same calendar year requirement), and provides context about CBSA definitions. However, it doesn't mention rate limits, pagination, or error behaviors that would be helpful for a data retrieval tool.
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?
Well-structured with purpose statement, parameter explanations with examples, and authentication note. Every sentence adds value, though the CBSA definition could be slightly more concise. The information is front-loaded with the core purpose first.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a data retrieval tool with 6 parameters, 100% schema coverage, but no output schema or annotations, the description provides good parameter context and authentication details. However, it lacks information about return format, data volume, or error handling that would help the agent use this tool effectively.
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 description adds substantial value beyond the 100% schema coverage by providing specific examples of parameter codes (e.g., 44201 for Ozone) and CBSA codes with real-world geographic mappings (e.g., 31080 for Los Angeles). This transforms abstract parameter definitions into practical, actionable information for the agent.
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 verb 'Get' and resource 'air quality monitors' with specific geographic scope 'in a Core Based Statistical Area (CBSA)', and explains what CBSAs are. It distinguishes from siblings like aqs_monitors_by_box, aqs_monitors_by_county, etc. by specifying the CBSA-based filtering approach.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the CBSA explanation and parameter examples, but doesn't explicitly state when to use this tool versus alternatives like aqs_monitors_by_county or aqs_monitors_by_state. It provides parameter guidance but no comparative usage recommendations with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses authentication behavior (environment variable fallback), date constraints (same calendar year), and return content (detailed monitor information). However, it doesn't mention rate limits, error handling, or pagination behavior, which would be helpful for a data retrieval tool.
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 clear sections (purpose, parameters, note) and every sentence adds value. It could be slightly more concise by integrating the parameter examples more tightly, but overall it's efficiently organized 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 8 parameters, no annotations, and no output schema, the description does a good job explaining inputs and purpose. However, it lacks information about the return format/structure, error conditions, or data volume limitations, which would be important for proper agent usage.
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?
Despite 100% schema description coverage, the description adds significant value by providing common parameter code examples (e.g., 44201 for Ozone), clarifying date format requirements, and giving concrete examples of state/county codes. This goes well beyond the schema's basic descriptions.
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 with specific verb ('Get') and resource ('air quality monitors at a specific monitoring site'), and distinguishes it from siblings by focusing on monitors rather than summaries or sample data. It also specifies what information is returned (location, operational dates, measurement parameters).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the parameter explanations (e.g., 'must be same calendar year'), but doesn't explicitly state when to use this tool versus alternatives like 'aqs_monitors_by_county' or 'aqs_monitors_by_state'. It provides authentication guidance but no comparative usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 key behavioral traits: it's a read operation ('Get'), requires authentication (implied by email/key parameters), returns potentially large data volumes ('This can return large amounts of data'), and provides data format details (arithmetic mean, maximum values, etc.). It doesn't mention rate limits, pagination, or error handling, but covers the essential behavior well for a data retrieval tool.
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 efficiently structured in three sentences: purpose statement, data content details, and important notes about requirements and data volume. Every sentence adds value without redundancy. It's appropriately sized 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.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a data retrieval tool with 6 parameters (4 required), 100% schema coverage, but no annotations or output schema, the description provides good contextual completeness. It covers what the tool does, what data it returns, authentication requirements, and data volume considerations. The main gap is lack of output format details, but given the complexity level and schema coverage, this is a minor omission.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing detailed documentation for all 6 parameters. The description adds minimal parameter semantics beyond the schema, only explicitly mentioning the state FIPS code requirement. It doesn't explain relationships between parameters or provide additional context about their usage. The baseline of 3 is appropriate when the schema does the heavy lifting.
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 specific action ('Get daily summary air quality data'), resource ('for all monitoring sites in a state'), and scope ('Daily summaries include arithmetic mean, maximum values, observation counts, and AQI values for each day'). It distinguishes from siblings by specifying 'daily' (vs annual/quarterly) and 'by state' (vs by box/cbsa/county/site).
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 for when to use this tool ('Get daily summary air quality data for all monitoring sites in a state') and mentions a constraint ('Requires state FIPS code'). It doesn't explicitly state when not to use it or name specific alternatives among the many sibling tools, though the 'daily' and 'by state' aspects implicitly differentiate it from annual/quarterly summaries and other geographic scopes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of behavioral disclosure. It effectively describes key behavioral traits: it's a read-only health check (implied by 'Check if...is operational'), discloses authentication behavior ('Credentials are optional - uses environment variables as fallback'), and hints at response behavior ('verifies the API is responding'). It doesn't mention rate limits or detailed error handling, but covers essential operational aspects.
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 efficiently structured in two sentences: the first states the purpose and context, the second clarifies authentication behavior. Every sentence adds value without redundancy, and it's front-loaded with the core functionality. No wasted words or unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (health check with 2 optional parameters), no annotations, and no output schema, the description is reasonably complete. It covers purpose, authentication behavior, and operational context. However, it lacks details on the return value (e.g., what 'operational' means in response format) and potential error scenarios, which would enhance completeness for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters (email and key) as optional with fallback to environment variables. The description adds no additional parameter semantics beyond what's in the schema, such as format examples or edge cases. Baseline 3 is appropriate when the schema does all the work.
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 specific action ('Check if...is operational') and resource ('EPA Air Quality System (AQS) API'), distinguishing it from all sibling tools which focus on data retrieval rather than health checks. It explicitly identifies this as a 'health check endpoint' that 'verifies the API is responding,' making the purpose unambiguous and distinct.
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 for when to use this tool ('Check if the EPA Air Quality System (AQS) API is operational'), implying it should be used for system status verification. However, it does not explicitly state when not to use it or name alternatives (e.g., other health checks or error-handling tools), which prevents a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing critical behavioral traits: the WARNING about potentially large data returns, the recommendation to limit date ranges, and clarification that it returns 'individual sample measurements' (not summaries). It doesn't mention rate limits or authentication requirements, but those are covered in 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with zero waste: first states purpose, second provides critical warning and recommendation, third clarifies return format. The warning is appropriately front-loaded, and every sentence adds essential information for tool selection and usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 9-parameter tool with no annotations and no output schema, the description provides good contextual completeness: it explains what the tool returns, warns about data volume, and gives usage recommendations. It could improve by mentioning authentication (though covered in schema) or providing more detail about the return structure, but it's largely adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds minimal parameter semantics beyond the schema—it mentions 'bounding box' and 'coordinates' which relate to the lat/lon parameters, but doesn't provide additional context about parameter interactions or usage patterns that aren't already in the schema descriptions.
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 verb ('Get raw sample data') and resource ('for all monitoring sites within a geographic bounding box'), distinguishing it from sibling tools like aqs_sample_data_by_cbsa or aqs_sample_data_by_site that use different geographic scopes. It specifies 'individual sample measurements' to differentiate from summary tools.
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 with a WARNING about data size and recommends limiting date ranges, offering practical guidance for when to use it cautiously. However, it doesn't explicitly mention when to choose this tool over alternatives like aqs_sample_data_by_cbsa or aqs_sample_data_by_site, missing explicit sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by warning about potential large data volumes and recommending date range limits. It also describes the return format (time, value, units, quality flags). However, it doesn't mention authentication requirements, rate limits, or error handling, which would be helpful for a data retrieval tool.
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 perfectly structured with a clear purpose statement, important warning, practical recommendation, and return format description - all in three efficient sentences. Every sentence adds value and the warning is appropriately front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a data retrieval tool with 8 parameters and no output schema, the description provides good context about data volume concerns and return format. However, it could benefit from mentioning authentication (implied by email/key parameters) and providing more guidance on when to use this versus aggregated summary tools. The schema documentation is excellent, which helps compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description doesn't add any parameter-specific information beyond what's already documented in the schema. It mentions date ranges generally but doesn't provide additional context about parameter relationships or constraints beyond the schema's excellent documentation.
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 verb ('Get raw sample data') and resource ('for a specific monitoring site'), distinguishing it from sibling tools like aqs_sample_data_by_box or aqs_sample_data_by_county that aggregate by different geographic boundaries. It specifies this returns individual sample measurements rather than summaries.
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 strong guidance on when to use this tool with the WARNING about data size and recommendation to limit date ranges to one week or one month. However, it doesn't explicitly mention when to choose this tool versus sibling tools like aqs_daily_summary_by_site or aqs_annual_summary_by_site that provide aggregated data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return format ('parameter codes and names') and their purpose ('use these codes in data queries'), which adds useful context. However, it lacks details on authentication requirements, rate limits, error handling, or pagination, leaving gaps for a tool that likely interacts with an external API.
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 front-loaded with the core purpose, followed by usage instructions and examples, all in three concise sentences with zero wasted words. Each sentence serves a clear function: stating the action, explaining the output, and guiding usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is mostly complete. It covers the purpose, output format, and usage flow with a sibling tool. However, it lacks details on authentication (implied by parameters but not explained) and behavioral aspects like error handling, which could be important for API interactions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters. The description adds value by explaining the purpose of the 'pc' parameter ('parameter class name') and providing examples (e.g., 'CRITERIA'), which helps the agent understand the context beyond the schema's technical details. It also clarifies the relationship with 'aqs_list_parameter_classes' for obtaining valid class names.
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 specific action ('Get a list of parameters'), identifies the resource ('within a parameter class'), and distinguishes it from siblings by specifying it returns parameter codes and names for use in data queries. It explicitly references the sibling tool 'aqs_list_parameter_classes' as a prerequisite, making its role distinct from other tools that fetch summary data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: after using 'aqs_list_parameter_classes' to get available class names, and it lists common parameter codes to illustrate typical use cases. It implicitly distinguishes from siblings by focusing on parameter listing rather than data retrieval, though it doesn't explicitly state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of behavioral disclosure. It effectively warns about potential data volume issues ('can be very large for metropolitan areas') and provides practical guidance on date range limitations. However, it doesn't mention authentication requirements, rate limits, or error handling, leaving some behavioral aspects uncovered.
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 efficiently structured with three sentences: purpose statement, warning with recommendation, and return value clarification. Every sentence adds value - the first establishes scope, the second provides critical usage guidance, and the third distinguishes output type. No wasted words or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a data retrieval tool with 6 parameters and no output schema, the description provides good contextual completeness. It covers the purpose, scope, data volume considerations, and output type. The main gap is the lack of output format description (structure of returned sample measurements), which would be helpful given no output schema exists.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, providing complete parameter documentation in the structured schema. The description adds minimal parameter semantics beyond the schema - it mentions CBSA and date ranges in the context of data volume warnings, but doesn't provide additional parameter meaning or usage examples beyond what's already in the schema descriptions.
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 verb 'Get' and resource 'raw sample data for all monitoring sites in a Core Based Statistical Area (CBSA)', specifying both the action and scope. It distinguishes from siblings like aqs_sample_data_by_site or aqs_sample_data_by_state by explicitly mentioning CBSA scope, and from summary tools by emphasizing 'raw sample data' versus aggregated summaries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance with a WARNING about data size and a strong recommendation to limit date ranges to one week or one month. It distinguishes this tool from summary tools by specifying it returns 'individual sample measurements' rather than aggregated data, helping users choose between raw sample vs summary tools in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing critical behavioral traits: the warning about potentially very large data returns, the recommendation to limit date ranges for practical use, and the authentication context (though authentication details are also in the schema). It doesn't mention rate limits or error handling, but covers the most important operational consideration.
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 perfectly structured with three sentences: purpose statement, critical warning with usage recommendation, and clarification of return type. Every sentence earns its place with no wasted words, and the warning is appropriately front-loaded for user awareness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a data retrieval tool with no annotations and no output schema, the description provides excellent context about what the tool returns ('individual sample measurements from all sites') and critical operational guidance. The main gap is lack of information about return format/structure, but given the complexity level and schema coverage, this is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter documentation.
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 verb ('Get raw sample data') and resource ('for all monitoring sites in a county'), making the purpose specific and actionable. It distinguishes from siblings like aqs_annual_summary_by_county by emphasizing 'raw sample data' and 'individual sample measurements' rather than aggregated summaries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool with the WARNING about data size and recommendation to limit date ranges to one week or one month. It implicitly distinguishes from summary tools by specifying it returns 'individual sample measurements' rather than aggregated data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of behavioral disclosure. It effectively warns about performance implications ('EXTREMELY large'), provides practical usage advice ('limit date ranges'), and clarifies the return format ('individual sample measurements'). However, it doesn't mention authentication requirements (covered in schema) or potential rate limits, leaving some gaps.
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 efficiently structured with three sentences: purpose statement, critical warning with recommendation, and return value clarification. Every sentence adds essential information without redundancy, and the warning is appropriately front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a data retrieval tool with no annotations and no output schema, the description provides strong context about data volume and usage constraints. It covers the core purpose and behavioral considerations well, though it doesn't describe the output structure (only mentions 'individual sample measurements'), which would be helpful given the lack of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the baseline is 3. The description adds minimal value beyond the schema by implying that parameters define the query scope (state, date range, pollutant), but doesn't provide additional semantic context like examples or edge cases beyond what's already documented in the 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 with specific verbs ('Get raw sample data') and resources ('for all monitoring sites in a state'), distinguishing it from sibling tools like aqs_sample_data_by_county or aqs_sample_data_by_site by specifying the geographic scope. It also explains what the data represents ('individual sample measurements from all sites').
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool with a strong warning about data size ('EXTREMELY large for state-level queries') and a specific recommendation to limit date ranges ('one week or less'). It implicitly distinguishes from summary tools (e.g., aqs_annual_summary_by_state) by emphasizing raw sample data, though it doesn't name alternatives directly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well: it discloses that this is a registration/mutation operation (not read-only), that it sends something to an email address, and that the output is an API key. However, it doesn't mention potential rate limits, confirmation requirements, or what happens if email is invalid.
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?
Two tightly focused sentences with zero waste. First sentence states purpose and parameters, second provides crucial prerequisite context. Every word earns its place in this efficient description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter registration tool with no output schema, the description is quite complete: it explains what the tool does, when to use it, and what happens. The main gap is lack of output format details (though 'API key will be sent' gives some indication), and no mention of error conditions or processing time.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the single parameter. The description adds marginal value by reinforcing that the email is where 'the API key will be sent,' but doesn't provide format requirements or validation rules beyond what the schema implies.
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 specific action ('Register for an EPA Air Quality System (AQS) API key') and resource ('API key'), distinguishing it from all sibling tools which are data query operations. It explicitly mentions this is for obtaining credentials needed for other operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'This key is required for all other AQS API operations' tells the agent when to use this tool (before any other AQS operations) and establishes its prerequisite role versus the data retrieval siblings.
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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