livedatalink

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

No annotations are provided, so the description carries full burden. It explicitly states the return values (US AQI, PM2.5, etc.) and health category rating, providing sufficient transparency for a read-only query tool without side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one sentence that lists the returned data, followed by a list of example queries. It is front-loaded with the tool's purpose and is highly concise with no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no output schema), the description fully covers what the tool does, what data it returns, and when to use it. No additional information is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'location' is described as 'City, zip code, or place name' in the schema. The description adds value by showing example queries that demonstrate valid inputs, going beyond the schema's bare description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves current air quality data for any location, listing specific pollutants and health ratings. It is distinct from siblings which focus on topics like colleges, courts, or weather.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a list of example queries that indicate when to use this tool (e.g., 'what's the air quality?'). While it does not explicitly state when not to use it or mention alternatives, the examples strongly imply appropriate contexts.

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

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

No annotations provided, so the description must carry the burden. It mentions data source dependency (DAPIP) which adds transparency, but omits details like read-only nature, authentication, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no unnecessary words. It efficiently conveys the tool's output.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the single parameter and no output schema, the description adequately describes the returned data. It mentions conditional availability (DAPIP publication) which adds context. Could be improved by noting the lookup is for a single institution.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a clear description for unit_id. The description does not add extra parameter meaning beyond what the schema provides, so baseline score applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns current institutional accreditation status, accreditor, last action date, and programmatic accreditations. It distinguishes from sibling tools like college_demographics or college_metrics which cover other aspects.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. The description does not mention scenarios where other college tools would be more appropriate.

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

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

No annotations are provided, so the description must fully disclose behavioral traits. It only mentions the input parameter and general comparison categories. It fails to state that the tool is read-only, what the output format is (e.g., structured data), or any constraints like rate limits or authentication.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: one sentence explaining purpose plus a short instruction. It is front-loaded with the core action. Every word is necessary with no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations or output schema, the description is incomplete. It explains what the tool does and the required input but does not describe the return format, pagination, or any additional context about how the comparison is presented. A more complete description would briefly outline the output structure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers the single parameter with a description of 'Array of 2-5 IPEDS UNITIDs.' The description adds 'Pass UNITIDs,' which is redundant. Since schema coverage is 100%, the description adds minimal value beyond restating the parameter's purpose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs a side-by-side comparison of 2-5 schools across cost, outcomes, and admissions metrics. The verb 'compare' and resource 'schools' are specific. It distinguishes from sibling tools like college_metrics (single school) and college_trends (time series).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly indicates when to use: when a comparative analysis of multiple schools is needed. It does not explicitly exclude alternatives or specify when not to use, but the context of 'comparison' against siblings like college_search or college_metrics is clear enough for an agent.

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

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

No annotations provided, so description carries full burden. It states the tool returns demographics but does not disclose behavioral traits such as read-only nature, data freshness, or any side effects. For a single-school lookup, likely safe, but not explicitly stated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence with no unnecessary words. Immediately conveys the tool's purpose and outputs. Highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the low complexity and sole parameter, the description adequately covers what the tool does. However, without output schema, it could include a note on return format or that data is for a single school. Mostly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single required parameter (unit_id). Description adds no additional meaning beyond the schema's 'IPEDS UNITID.' Baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool returns student-body demographics for one school, listing specific categories (race/ethnicity, gender, age, geographic origin). Distinguishes from siblings like college_search or college_compare which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implied usage for obtaining detailed demographics of a single school, but no explicit guidance on when to use this tool versus siblings like college_metrics or college_outcomes_by_program. No exclusion criteria or alternatives mentioned.

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

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

No annotations are provided, and the description fails to disclose behavioral traits such as data source, update frequency, or limitations (e.g., only public institutions). It assumes read-only context but does not state it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, front-loaded with verb 'cost and outcome metrics', minimal waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description lists the metrics returned. However, it does not specify time frame, data vintage, or restrictions (e.g., only institutions in IPEDS), leaving some gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes the single parameter (unit_id) as IPEDS UNITID. The description adds no further meaning, but schema coverage is 100%, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly enumerates specific metrics (tuition, net price, graduation rate, etc.) for a single school, distinguishing it from sibling tools like college_search and college_compare.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for one school with a unit_id, but does not provide explicit when/when-not guidance or mention alternatives like college_compare for multiple schools.

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

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

No annotations provided, so the description bears full burden. It discloses what data is returned but omits behavioral traits like read-only nature, rate limits, error conditions, or data freshness. The description is minimal for a tool that likely returns multiple records per school.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence of 21 words, front-loaded with key information. No unnecessary words or repetition. Every word adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one param, no output schema), the description is fairly complete. It specifies the data types returned. However, it could mention that the output likely contains multiple programs and that the data pertains to a specific year or CIP code range.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single parameter (unit_id). The tool description adds context about the returned data but not about the parameter itself. Baseline 3 is appropriate as schema already documents the parameter adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides program-level outcomes for one school, specifying median earnings, median debt, and award counts. It uses a verb (implied 'get') and resource (program-level outcomes), distinguishing from sibling tools like college_demographics or college_metrics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies it is for program-level outcomes per school, but does not explicitly state when to use it versus siblings or provide exclusions. Context signals and sibling names offer implicit distinction, but no direct guidance.

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

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

No annotations are present, and the description does not disclose any behavioral traits such as data source, freshness, rate limits, or behavior on empty results. For a search tool, this lack of transparency leaves agents uncertain about reliability.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two well-structured sentences with no wasteful words. It front-loads the main action and then lists filters and return fields efficiently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the six optional parameters and lack of output schema, the description provides a solid overview of functionality and return data. It could mention pagination or default behavior, but it is largely sufficient for an agent to understand the tool's purpose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds value by listing the return fields (location, control, etc.) not in the schema, but does not elaborate on parameter semantics beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it searches US colleges and universities by various filters and lists the return fields. It effectively communicates the core function, though it does not explicitly differentiate from sibling tools like college_compare or college_demographics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool instead of siblings. With many related college tools on the same server, explicit context about when to choose college_search over alternatives would be very helpful.

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

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

With no annotations, the description carries the full burden. It mentions the data source (Urban Institute Education Data Portal) but fails to disclose behavioral traits such as read-only nature, rate limits, data freshness, or whether the tool returns aggregated or per-year data. This is a significant gap 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, information-dense sentence that front-loads the purpose and essential choices (metric and year range). No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description adequately explains what the tool does but is incomplete regarding the output format. With no output schema, the agent needs to infer that the result is a time-series of the chosen metric. A brief mention of the output structure would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions for each parameter. The description adds context by grouping metric choices and implying a year range, but does not provide new information beyond the schema. Baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Multi-year trend for one school', specifying the source (IPEDS), the metrics (enrollment, graduation_rate, retention, cost), and the year range. This effectively differentiates it from siblings like college_metrics (single-year data) and college_compare (comparison across schools).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for obtaining trend data for a single school over time, but does not explicitly state when to use this tool versus alternatives (e.g., college_metrics for single-year data, college_compare for cross-school comparisons). 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.

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

No annotations are provided, so the description carries full burden. It lists the return fields but does not disclose error handling (e.g., invalid symbol), data freshness, or authentication requirements. The description is adequate but lacks depth for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first concisely states purpose and outputs, the second gives usage examples. Every sentence earns its place with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite lacking an output schema, the description enumerates many return fields (sector, revenue, EPS, etc.) and provides example queries, making it complete for a fundamentals tool. Minor gaps include data scope (e.g., only US stocks?) and freshness, but overall sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with the single parameter 'symbol' having a clear description ('Stock ticker symbol (e.g., "AAPL")'). The tool description adds no further semantics beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states what the tool does: 'Get company profile and financial fundamentals.' It lists specific financial metrics and provides example queries. It distinguishes itself from siblings like stock_quote (which focuses on price) by emphasizing comprehensive fundamentals.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides multiple example queries ('tell me about Apple', 'what does this company do?', etc.) that clearly indicate when to use this tool. However, it does not explicitly exclude cases where a simpler sibling (e.g., stock_quote for price or stock_history for historical data) might be more appropriate.

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

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

With no annotations, the description bears full responsibility for behavioral disclosure. It implies a read-only search operation but does not explicitly state safety traits (e.g., no side effects, no destructive actions). No rate limits or authentication needs are mentioned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description consists of two concise sentences: the first explains the purpose and filters, the second describes the output. No unnecessary text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema, the description should detail the return format more thoroughly. It only mentions 'case summaries with citations,' missing pagination, fields, or result count. The description is adequate but not fully complete for a tool with 7 optional parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and each parameter has its own description in the schema. The tool description merely lists the search dimensions without adding additional meaning or clarification beyond the schema, so it provides no extra value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it searches federal and state court opinions by multiple filters and returns summaries with citations. However, it does not differentiate itself from the sibling tool 'court_opinion_search', which may cause confusion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus the similar sibling 'court_opinion_search' or any other alternatives. There is no mention of prerequisites or context for use.

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

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

The description provides no behavioral details such as error handling, expected output structure, or whether the operation is read-only. Without annotations, this is a significant gap for a single-purpose tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, concise and front-loaded. However, it could earn its place by adding more useful context, such as expected output format, without becoming verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the low complexity (1 required parameter) and no output schema, the description should provide what the tool returns (e.g., case details) but does not. This leaves the agent uninformed about the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a description for the citation parameter. The tool description repeats the example but adds no additional meaning beyond the schema, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool resolves a legal citation to a CourtListener case record, with an example. It differentiates from sibling tools like court_case_search (which searches by other criteria) by specifying the input is a citation string.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like court_case_search or court_opinion_search. The description implies usage when a citation string is available, but lacks explicit direction or exclusions.

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

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

With no annotations, the description bears the full burden of behavioral disclosure. It states the tool returns party list and recent entries from PACER/RECAP, which adds useful context about data source and output nature. However, it does not mention side effects, error handling, or any constraints (e.g., rate limits, data recency), so transparency is adequate but incomplete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, each adding essential information: what the tool does and what it returns. No redundant or filler content. It is efficiently structured and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (two parameters, no output schema, no annotations), the description covers the basic functionality and output. However, it lacks contextual details such as when to prefer this tool over siblings, potential error scenarios, or scope limitations (e.g., only federal dockets). This makes it minimally viable but not fully comprehensive for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, meaning both parameters are already described in the input schema. The description provides examples (e.g., 'nysd' and '1:23-cv-04567') that add marginal clarity but do not significantly extend beyond the schema's own descriptions. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool looks up a federal docket by court ID and docket number and specifies it returns party list and recent entries from PACER/RECAP. However, it does not explicitly differentiate from sibling tools like court_case_search or court_opinion_search, which may have overlapping functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as court_case_search or court_opinion_search. There is no mention of prerequisites, limitations, or exclusions, leaving the agent without context for appropriate invocation.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool returns (positions, education, bench history) but does not mention whether it is read-only, if it requires authentication, any rate limits, or how it handles partial name matches. This is minimal transparency for a lookup tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that efficiently conveys the tool's purpose and output. Every word adds value, and there is no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter, no nested objects, no output schema), the description adequately covers what the tool does and what it returns. It is nearly complete, though it might benefit from noting that partial name searches are supported, which the schema already implies.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage for the single parameter 'name', which is well-described in the schema itself. The description adds no additional semantic information beyond what the schema already provides, so a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'look up' and the resource 'judge profile', specifying that it can be done by name or CourtListener person ID. It distinguishes from sibling tools by focusing specifically on judge profiles, unlike other court-related tools that handle cases, dockets, opinions, etc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly indicates use when seeking a judge's profile, but it provides no explicit guidance on when not to use this tool or mentions alternatives among the many sibling tools. No exclusions or context for optimal use are given.

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

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

With no annotations, the description carries full burden. It only mentions outputs but does not disclose behavioral aspects like search behavior (e.g., fuzzy matching), pagination, authentication requirements, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, well-front-loaded sentence conveys the purpose and key outputs with zero waste. Every word serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a simple 3-parameter tool with no output schema or annotations, the description adequately states purpose and outputs but lacks detail on result ordering, pagination, and expected behavior for edge cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for all parameters. The description adds no additional meaning beyond the schema, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it performs full-text search of court opinions and lists specific outputs (snippets, authors, citations), distinguishing it from sibling tools like court_case_search which likely search case metadata.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives (e.g., court_case_search, court_docket_lookup). The description does not provide any context for tool selection.

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

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

Despite no annotations, the description clearly states the output (audio URLs and transcript snippets), which is sufficient for a straightforward search tool with no side effects. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that efficiently conveys the tool's purpose and output, with no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description explains returns (audio URLs and transcript snippets) and covers the search functionality. It lacks output structure details, but given no output schema, it is reasonably complete for a simple search tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the baseline is 3. The description adds context about the court parameter implying federal appellate, but does not provide additional meaning beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool searches for SCOTUS and federal appellate oral argument audio, clearly distinguishing it from sibling tools like court_opinion_search. The verb 'Search' and resource are specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context that the tool is for oral argument audio, implying its use case. However, it does not explicitly mention when not to use it or direct to alternatives among the many court-related siblings.

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

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

No annotations are provided, so the description carries full burden. It reveals only that results are ordered newest first. It does not disclose whether authentication or payment (e.g., PACER) is needed, rate limits, or the structure of returned data. Critical behavioral traits are missing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, no filler, front-loaded with key action and scope. Every word earns its place; it is concise without being under-specified.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema, the description should explain what a docket entry contains (e.g., case number, date, party names). It does not. Also, the 'court' parameter uses abbreviations like 'nysd' without explanation. Given the complexity of legal data, the description is insufficient for an agent to fully understand what the tool returns.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so both 'court' and 'limit' are documented in the input schema. The description adds no additional meaning to the parameters beyond the ordering hint. According to guidelines, baseline is 3; the slight extra context (ordering) does not elevate the score significantly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns recent docket entries for a specific court, ordered newest first. It uses a specific verb ('recent docket entries filed') and resource ('in a specific court'), and among siblings like court_docket_lookup (for a specific docket) and court_case_search (for case lookup by criteria), it uniquely identifies its function as browsing recent activity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus alternatives. Sibling names imply different purposes, but the description provides no direct guidance on when to choose this over court_docket_lookup, court_opinion_search, etc. The usage is implied but not clarified.

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

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

No annotations are provided. The description describes the output (comparison table with specific fields) but does not mention read-only nature, error handling, or rate limits. Adequate for a simple read tool but could add more behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences plus example queries. Front-loaded with action and output; every sentence adds value. No unnecessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, the description fully covers purpose, input format, output contents, and example queries. No gaps for an AI agent to select or invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description covers the single parameter ('coins') with format 'Comma-separated coin names or tickers, 2-5 coins'. The tool description reinforces this without adding new details. Schema coverage is 100%, baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool compares 2-5 cryptocurrencies and lists the specific metrics shown (price, 24h change, market cap, volume, rank). It provides example queries differentiating it from single-coin tools like crypto_price or crypto_info.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use this for... any multi-coin comparison question,' implying when to use. It does not explicitly say when not to use, but the examples and phrase 'multi-coin' sufficiently guide the agent.

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

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

No annotations are provided, so the description carries the full burden. It only lists what data is returned but does not disclose any behavioral traits such as whether the operation is read-only, any side effects, required permissions, rate limits, or response size. The read-only nature is inferred but not stated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one concise sentence followed by a list of example queries, which is efficient and front-loaded. The examples are helpful and not excessive. Minor room for improvement: could be even shorter if examples were omitted, but they add value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description lists all major fields returned (description, market data, supply info, all-time high/low, genesis date, blockchain, categories, website links), making the return value clear. For a simple tool with one parameter, this is complete and sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single parameter 'coin', and the schema description already provides clear semantics ('Cryptocurrency name or ticker'). The function description adds no extra information about the parameter, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets a detailed profile for any cryptocurrency, listing many fields (description, market data, supply info, etc.). It distinguishes from sibling tools like crypto_price, crypto_compare, and crypto_trending by emphasizing 'profile' and 'background information'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage examples ('tell me about bitcoin', 'what is ethereum?', etc.) that make it clear when to use this tool. However, it lacks explicit when-not-to-use guidance or direct comparison to alternatives, though the examples implicitly cover the scope.

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

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

No annotations are provided; the description explains what data is returned (price, change, market cap, etc.) but does not discuss limitations, rate limits, or data freshness. Moderate transparency for a read-only tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that effectively communicates purpose and usage, but it is verbose with a long list of example queries. Front-loaded with the key action but could be more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple input schema and no output schema, the description covers what the tool does and what it returns. It mentions return fields and supported cryptocurrencies, providing adequate context for selecting and using the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'coin' is described in the schema as name or ticker, and the description adds value by listing examples and stating support for thousands of coins via CoinGecko ID, enriching the basic schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets price and market data for any cryptocurrency, listing specific data points (price in USD, 24h change, etc.) and numerous example queries, distinguishing it from siblings like crypto_compare and crypto_trending.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides extensive example queries (e.g., 'what's the price of bitcoin?') that indicate when to use the tool. It does not explicitly exclude alternative tools, but the examples make usage context clear.

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

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

No annotations provided, but the description explains it returns trending coins based on search activity. It doesn't mention any side effects or limitations like data latency, but for a simple read-only tool this is adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is slightly verbose with many example queries, but it's well-structured and front-loaded with the core purpose. Each example sentence serves to clarify usage.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters and no output schema, the description is complete: it identifies the data source, the criterion (search activity), and provides usage examples. No missing information for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has zero parameters, so description compensates fully by explaining what the output contains. Schema coverage is 100% trivially.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns top trending coins on CoinGecko based on search activity. It differentiates from sibling tools like crypto_price or crypto_compare by focusing on trending interest rather than prices or comparisons.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit example queries (e.g., 'what's trending in crypto?') but does not state when not to use this tool or mention alternatives. However, the usage is clearly implied for trending-related questions.

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

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

No annotations provided, so description carries full burden. It discloses return fields (CVSS scores, weakness IDs, references, affected products) and source (NVD). However, does not mention error handling or rate limits, which is acceptable for a simple lookup tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences efficiently cover purpose, ID format, and return content. No extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple lookup tool with one parameter and no output schema, the description adequately explains purpose, input format, and returned data. Sibling tools are relevant but don't require explicit comparison.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with one parameter described as 'CVE identifier, e.g. CVE-2024-3094.' Description reinforces the ID format but adds minimal new meaning beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it provides full detail for a single CVE by ID, specifies the ID format (CVE-YYYY-NNNN), and lists the returned data (CVSS scores, weakness IDs, references, affected products). This distinguishes it from sibling tools like cve_recent and cve_search_by_keyword.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implicitly guides usage by specifying 'single CVE by ID', but does not explicitly state when to use this tool versus alternatives like cve_search_by_keyword or cve_recent. No when-not or alternative tool mentions.

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

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

No annotations are provided, so the description must disclose behavioral traits. It only mentions parameter defaults and filters but does not specify output format, pagination, rate limits, or whether the tool is read-only. This is insufficient for an agent to understand the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is front-loaded with the core purpose. Every word is functional, and no superfluous information is included.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no description of the return format, the agent lacks information about what the tool returns (e.g., fields in each CVE entry). The description only covers input parameters, leaving a significant gap for a tool that outputs a list of CVEs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already describes each parameter. The description adds the default and max value for 'days' and lists the severity values. This provides some additional context but does not significantly enhance understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves recent CVEs with a configurable lookback window (default 7, max 120 days) and optional vendor/severity filters. This distinguishes it from sibling tools like cve_lookup (specific CVE) and cve_search_by_keyword.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving recent CVEs with optional filters, but does not explicitly state when to use this over alternatives like cve_search_by_vendor or cve_search_by_keyword. No exclusions or when-not guidance is provided.

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

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

No annotations are provided, so the description carries full burden. It only states that the search matches keyword against description text with optional date range. No behavioral traits like rate limits, data freshness, or authentication needs are disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences that front-load the core purpose. No redundancy or unnecessary details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the basic functionality but lacks information about output format, error handling, or result structure. Since there is no output schema, more context about what is returned would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description merely summarizes parameter purposes without adding significant new meaning beyond the schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'search', the resource 'CVE', and the scope 'free-text against CVE description text'. It effectively distinguishes from sibling tools like cve_lookup (by ID) and cve_search_by_vendor (by vendor).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage context is implied by the description and sibling names, but no explicit guidance on when to use this vs alternatives is provided. The description does not mention when-not-to-use or suggest other tools.

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

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

No annotations are provided, and the description does not disclose behavioral traits such as whether the tool is read-only, rate limits, or output format. Beyond the search action, no transparency is offered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first states purpose, second provides a key detail. Front-loaded and no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema and no description of return values or pagination. Useful for a search tool but lacks completeness regarding what the response contains.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents each parameter. The description adds context about vendor matching against the NVD CPE namespace, which adds value beyond the schema's basic description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the action 'Search CVEs' and the resource, with specific filters (vendor, product, date range). Distinguishes itself from siblings like cve_search_by_keyword.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives like cve_search_by_keyword or cve_lookup. Provides a useful note about vendor namespace matching but lacks when-not or comparative advice.

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

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

No annotations are provided, so the description carries full burden. It states what the tool returns but does not disclose error behavior for invalid IDs, data freshness, or potential side effects. For a simple lookup, this is acceptable but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the core action ('MITRE CWE detail by ID') and then lists the returned fields. No filler or unnecessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no output schema), the description adequately covers purpose and return fields. However, it lacks details on error handling or the structure of parent/child relationships, which could be important for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes the cwe_id parameter with an example. The description adds value by clarifying acceptable formats ('CWE-NNN or NNN'), which aids correct invocation. This exceeds the baseline of 3 for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: retrieving details for a specific CWE ID. It specifies the output fields (name, abstraction, etc.), but does not differentiate from sibling tools like cve_lookup or cwe_recent, which could cause confusion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when a CWE ID is known, but does not explicitly state when to use this tool versus alternatives (e.g., cwe_recent for recent CWEs) or when not to use it. No guidance on prerequisites or error handling.

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

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

No annotations provided, so description must convey behavioral traits. It lacks details like recency of data, pagination, rate limits, or default ordering. Only mentions output fields, missing crucial context for an agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two efficient sentences with front-loaded purpose and output details. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose and output but lacks full behavioral context given six parameters, no output schema, and no annotations. Gaps remain in how limits, ordering, and error handling work.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline 3. Description briefly mentions filters (state, county, incident type, date range) but adds no new semantics beyond existing parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it returns recent FEMA disaster declarations with filtering options and specifies output fields. However, it does not differentiate from siblings like disaster_history_summary, which might serve similar purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: use when you need filtered recent disaster declarations. No explicit when-not-to-use or alternative tools mentioned, leaving some ambiguity.

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

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

No annotations provided, so description carries full burden. It mentions bucketing by incident type and year, indicating aggregation, but lacks details on data sources (FEMA), coverage limits (federal only), or return format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero waste. The first sentence states the action, the second adds target audience and integration context. Front-loaded and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description hints at bucketed results but doesn't specify structure. For a summary tool, more detail on output would be beneficial, but it suffices given high schema coverage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions for each parameter. The description adds context about the tool's purpose but does not elaborate on parameter specifics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides a multi-year FEMA disaster summary for a location, bucketing declarations by incident type and year. It distinguishes itself from siblings like 'disaster_declarations' and 'flood_zone_lookup' by targeting cumulative risk assessment for insurance brokers and realtors.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description identifies target users (insurance brokers, realtors) and implies use with 'property_lookup' address. However, it does not explicitly state when not to use or compare to alternatives like 'disaster_declarations'.

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

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

No annotations are provided, and the description does not disclose behavioral traits such as read-only nature, data source recency, rate limits, or whether the operation is safe or destructive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, front-loaded with the core purpose, and efficiently lists filtering criteria without extra verbiage.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter tool with no output schema and no annotations, the description is too brief, missing explanation of output format, combining filters, ordering, and other behavioral details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is high (75%), so the baseline is 3. The description adds some context (e.g., 'region' grouping) but incorrectly mentions 'state name' which is not a parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns recent earthquakes from USGS and lists filtering options (region, magnitude, time), making it distinct from sibling tools which cover other domains.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or context for use.

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

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

No annotations exist, so description must disclose behavior. It states it returns a probability and percentile, implying a read-only query. However, it does not explicitly confirm it is non-destructive, which would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single, clear sentence that efficiently conveys the tool's purpose and output. No unnecessary words; front-loaded with the key action ('FIRST EPSS exploit prediction score').

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, the description fully covers what the tool does and what it returns. No additional context is necessary.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with the parameter 'cve_id' described as 'CVE identifier.' The description adds no further meaning to the parameter, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Describes exactly what the tool does: retrieves the FIRST EPSS exploit prediction score for a given CVE, including probability and percentile rank. It is specific and distinguishes from sibling CVE tools like cve_lookup by focusing on the prediction score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool vs alternatives. Usage is implied by the tool name and description, but no exclusions or prerequisites are provided.

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

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

With no annotations, the description should disclose behavioral traits but only lists return fields. Missing details on data freshness, rate limits, accuracy, or required permissions, which is a significant gap for a data lookup tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single efficient sentence listing outputs. While concise, it could be improved with bullet points for readability. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a lookup tool with no output schema and three parameters, the description covers purpose and outputs but omits parameter interaction rules, error cases, and data source details. Adequate but not thorough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is low (33%): only 'location' has a description. The description does not clarify the relationship between lat/lon and location, or which parameters are preferred. No additional meaning beyond the schema is provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns FEMA flood zone designation for an address or coordinate, listing specific output fields (zone code, BFE, FIRM panel, insurance mandate). This distinguishes it from siblings like nfip_flood_claims and disaster-related tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for flood zone lookups but does not explicitly state when to use this tool versus alternatives (e.g., property_lookup). No when-not or situational guidance is provided.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It indicates the tool returns status fields, but it does not disclose any side effects, error handling (e.g., invalid DOT number), data freshness, rate limits, or authentication requirements. This lack of detail limits transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that front-loads the core purpose. It includes a list of example questions, which is helpful but slightly verbose. No wasted sentences, but could be trimmed for brevity without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity of the tool (one parameter, no output schema, no annotations), the description covers the return fields and typical use cases. However, it lacks guidance on error handling, data format, or edge cases like expired authority. It is adequate but not fully comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'dot_number' is straightforward, but the description does not describe it at all (schema coverage 0%). It could add context like 'USDOT number of the carrier' or format expectations. Since the schema alone is minimal, the description should compensate but does not.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: checking legal authorization and insurance for trucking companies. It lists specific status fields and example questions, making the intent obvious. However, it does not explicitly differentiate from sibling tools like fmcsa_carrier_lookup, which may also provide basic carrier info, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides example questions that imply when to use the tool (e.g., 'can this carrier legally operate?', 'do they have insurance?'), which guides the user. But it does not explicitly state when not to use it or mention alternative tools for other needs like safety scores or detailed company info.

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

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

No annotations are provided, so the description carries the full burden. It describes return fields but does not disclose behavioral traits like whether it is read-only, rate limits, or authentication requirements. The read-only nature is implied but not explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences and lists key output fields and examples. It is efficient but slightly long; every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description adequately covers the tool's purpose, input constraints, and output fields. It does not detail error handling or prerequisites, but is sufficient for an agent to understand when and why to use it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one parameter (dot_numbers) with 0% description coverage. The description implies the parameter is DOT numbers but does not explicitly state it or provide additional meaning beyond the context. Since the parameter is simple and the tool name is clear, the baseline is adequate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool compares 2-5 trucking companies on safety, fleet size, insurance, and authority, and lists the output fields. It distinguishes itself from siblings like fmcsa_carrier_lookup and fmcsa_carrier_search by focusing on comparison.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides example questions ('which carrier is safer?', 'compare these trucking companies') that illustrate when to use the tool. It does not explicitly exclude alternatives but the context is clear.

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

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

No annotations are present, so the description must convey behavior. It clearly states this is a lookup (read operation) and lists the types of data returned. There is no mention of destructive actions, authentication, or rate limits, but for a simple read tool this is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main purpose and then lists many returned fields. While informative, it is somewhat verbose for the agent. Still, every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema, the description fully explains what the tool returns and covers the necessary context for a lookup tool (carrier identification and data fields). The agent can correctly select this tool over siblings based on the description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, so the description adds meaning by stating that lookups are performed 'by DOT number or MC number'. However, it does not clarify that only one should be provided or explain the difference between the two parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with a clear action verb 'Look up' and specifies the resource: a trucking company by DOT or MC number. It lists returned data fields, distinguishing it from sibling tools like fmcsa_carrier_search (which likely finds carriers by name) and fmcsa_carrier_compare (which compares multiple carriers).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides example queries ('is this carrier safe?', 'look up this trucking company', etc.), telling the agent when to use it. It does not explicitly mention when not to use it or alternatives, but the context of sibling tools provides implicit guidance.

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

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

With no annotations, the description carries the full burden. It discloses the search returns up to 50 matching carriers, the data fields included, and that it supports partial matching. No contradictions or omissions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, front-loaded with the action, efficient. Every sentence adds value, no unnecessary details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple input schema and no output schema, the description adequately covers the tool's purpose, return fields, result limit, and example queries. Missing error handling but acceptable for a search tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'name' is described as 'company name' with support for partial matching, adding value beyond the schema (which only specifies length constraints). For a simple string param, this is sufficient.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches for trucking companies by name, returning DOT number, MC number, location, fleet size, and operating status. It distinguishes itself from sibling tools like fmcsa_carrier_lookup and fmcsa_safety_scores by being a general name-based search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit example queries (e.g., 'find this trucking company', 'what's the DOT number for Werner?') and notes partial name matching. It does not directly compare to siblings but implies the tool is for searching by name rather than specific lookups.

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

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

No annotations are provided, so the description carries full behavioral disclosure burden. It explains that higher percentile = worse record and exceeding threshold = intervention alert. However, it does not mention rate limits, authentication, or any side effects beyond read operations. Adequate but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (less than 100 words) and front-loaded with the main action. It then lists outputs, provides example queries, and gives interpretation guidance. Every sentence serves a purpose without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given one parameter, no output schema, and no annotations, the description provides a good overall picture: what it returns (percentile rankings), how to interpret them, and example use cases. It could be slightly more thorough by explaining the threshold levels or linking to more details, but it is sufficient for the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% (no description for dot_number in schema), so the description must compensate. It mentions 'by DOT number' but does not explain what a DOT number is, its format, or required length. The schema indicates integer with exclusiveMinimum 0, which the description does not reiterate. Minimal added value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get safety scores and safety ratings for a trucking company by DOT number.' It lists specific outputs (BASIC percentile rankings) and distinguishes itself from sibling tools like fmcsa_carrier_lookup by focusing on safety evaluation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit example questions for when to use the tool (e.g., 'is this carrier safe to use?', 'check carrier safety record'). It does not explicitly state when not to use it or mention alternative tools, but the context is clear enough for an agent.

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

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

No annotations are provided, so the description must disclose behavioral traits. It implies a read-only query (fetching data), but does not explicitly state that it has no side effects or is non-destructive. A more explicit statement would improve transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that efficiently conveys the tool's purpose and output contents. It is front-loaded with key information and contains no superfluous text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description fully explains what the tool returns (category, wind/pressure, position, movement, forecast link). Combined with a single optional parameter, the description is complete for an agent to understand the tool's functionality.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with the single optional 'basin' parameter already described in the schema. The description adds no additional meaning beyond stating it is an optional filter, so it meets the baseline but does not exceed it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides currently-active hurricanes and tropical systems from NOAA NHC, including specific data fields like category, wind/pressure, position, movement, and forecast link. This distinguishes it from all sibling tools, which cover unrelated domains.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for querying current hurricane information. While it doesn't explicitly state when not to use it or mention alternatives, the context of sibling tools (all unrelated) makes it clear that this is the appropriate tool for tropical cyclone data.

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

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

With no annotations, the description conveys the behavior (returns specific fields), but does not disclose any additional traits such as rate limits, authentication needs, or what happens on unsuccessful checks. It is adequate but lacks extra context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences; front-loaded with the core purpose and efficient listing of return fields. No extraneous text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description helpfully lists the return fields (date added, due date, ransomware, required action). It is mostly complete for a simple check tool, though it lacks information on error responses or missing CVE handling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with one parameter 'cve_id' described as 'CVE identifier.' The description adds no new meaning beyond the schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool checks if a CVE is in the CISA Known Exploited Vulnerabilities catalog, specifying the resource (CVE) and action (check). It differentiates from siblings like cve_lookup by focusing on a specific catalog and listing distinct return fields.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the description (check CISA KEV status), but there is no explicit guidance on when to use vs. not use, or mention of alternatives among the many CVE-related siblings.

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

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

No annotations are provided, so the description carries full burden. It discloses the return fields (name, type, address, phone, website, hours, cuisine, distance), giving good transparency about the tool's behavior. It does not mention side effects or authentication needs, but as a search tool, those are less critical.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first states the tool's purpose and return fields; the second provides numerous specific examples. It is front-loaded, concise, and contains no superfluous information. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description explains the return fields and supports many categories. With 3 parameters and no output schema, the description covers the main aspects. It does not mention pagination or result limits, but given the tool's simplicity, this is acceptable. Nearly complete for its complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the input schema already documents all three parameters. The description adds some value by listing example queries and stating the default radius (1.5 miles), but the underlying schema already describes radius and location. Thus, the description provides marginal additional meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool finds local businesses and places near a location, listing many supporting categories and example queries. It is distinct from the sibling tools on the server, which focus on specialized domains like college data, courts, weather, stocks, etc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides numerous concrete examples of queries, effectively guiding the agent on when to use the tool (e.g., 'find restaurants near me', 'coffee shops in downtown Houston'). It does not explicitly state when not to use or mention alternatives, but the context is clear enough given the sibling tools are for different domains.

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

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

No annotations provided, so description carries full burden. It lacks disclosure of rate limits, authentication needs, data freshness, or what 'claim history' specifically includes. Brief description insufficient for behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose, followed by use case. Concise and efficient, though could incorporate more value without significant length increase.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has 6 optional parameters and no output schema. Description fails to explain default behavior (e.g., what if no params provided?), output format, or filtering logic. Incomplete for tool complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline of 3 applies. Description does not add additional meaning beyond schema; it mentions zip, county, state but no interaction details or constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'claim history aggregated by zip, county, or state' and identifies target users. However, it does not explicitly differentiate from sibling tools like flood_zone_lookup.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implied usage context ('for insurance brokers and homebuyers assessing prior loss patterns') but no explicit when-to-use, when-not-to-use, or alternatives provided.

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

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

No annotations provided, so the description must disclose behavioral traits. It only mentions 'currently-active' but lacks details on data freshness, rate limits, or behavior when no alerts exist. Minimal transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single, concise sentence that front-loads the key action and resource. No wasted words; every part adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, yet the description does not explain the return format or content of alerts. It covers input options but leaves gaps about output structure and pagination.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds that lat/lon define a point and that zone and state are alternative inputs, but does not elaborate on the location parameter beyond schema. With 60% schema coverage, it adds some context but not significantly more than the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves currently-active National Weather Service alerts for specific input types (point, state, zone), listing example alert types. It is specific and distinguishes from siblings like weather_current and weather_forecast.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for active alerts but does not explicitly state when to use this tool versus alternatives or provide exclusions. Given sibling tools, the context is somewhat implicit but insufficient.

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

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

No annotations are provided. The description does not disclose any behavioral traits such as data freshness, authentication needs, rate limits, or whether the operation is read-only. For a retrieval tool, it is minimal but acceptable, yet lacks explicit safety cues.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: the first defines purpose and data, the second provides usage examples. Front-loaded and no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 3 parameters and no output schema, the description covers the returned data fields and typical use cases well. However, it omits details like pagination, sorting, or limits, which are not critical for basic use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% description coverage for its 3 parameters (symbol, type, expiration). The description adds value by listing returned data fields but does not provide additional parameter-level semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool retrieves options chain data for a stock, listing specific data fields (strike prices, bid/ask, volume, open interest, implied volatility, expirations) and providing example queries that differentiate it from siblings like stock_quote.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description includes concrete usage examples ('show me AAPL options', 'what are the puts on Tesla?') indicating when to use the tool, but does not explicitly mention when not to use or alternative tools.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool auto-detects carriers and returns delivery status, current location, estimated delivery date, and tracking history. It does not mention error handling (e.g., invalid tracking numbers), rate limits, or authentication, which is acceptable for a simple tracking tool but could be more thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is highly concise: three sentences front-loading the purpose, followed by examples. Every phrase adds value, such as listing supported carriers and return fields. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately explains return values (delivery status, location, estimated date, history). For a single-parameter tool with no nested objects, this is sufficient for an agent to understand the tool's capabilities and invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already has 100% description coverage for the single parameter ('tracking_number'). The description adds context about auto-detection and usage examples but does not significantly enhance the meaning beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action ('Track a package or shipment by tracking number'), identifies the resource (tracking number), and lists specific use cases like 'where is my package?', effectively distinguishing it from all 70+ sibling tools, none of which are for package tracking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool ('for any package tracking question') and provides concrete example queries. It also notes that carrier detection is automatic. However, it does not mention when not to use it or refer to any alternative tools, though none are relevant.

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

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

No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits such as rate limits, authentication requirements, or side effects. The description only covers operational aspects, not behavioral details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that is reasonably concise, but it could be more structured (e.g., bullet points for return fields). It front-loads the main action and provides examples, earning its length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema and annotations, the description comprehensively covers the tool's purpose, input parameters, return fields, and geographic scope. It is complete enough for an agent to select and use the tool correctly, though missing behavioral details like pagination or error handling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the description adds value by explaining the query parameter can be a street address or account number, providing concrete examples, and stating the default county. This goes beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool looks up real estate property data by street address or account number, lists specific return fields, and provides example queries. It distinguishes itself from siblings like property_search_area and property_search_owner by focusing on direct lookup rather than search by area or owner.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives example use cases and questions, implying when to use the tool, but does not explicitly state when not to use it or mention alternative sibling tools. No exclusions or comparisons are provided.

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

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

No annotations are provided, so the description carries full burden. It discloses the return type (list of properties with addresses, owners, values, property types) and that it filters by geographic criteria. It does not mention rate limits or side effects, but for a read-only search tool this is acceptable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no redundant information. Front-loaded with the primary action and resource, then filter details, then example queries, then return type. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's functionality, filters, example usage, and return data. It lacks mention of result limits or pagination, but given no output schema, it provides enough for an agent to understand what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description reiterates parameter names and briefly adds context (e.g., '5-digit ZIP code') but does not add significant new meaning beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description starts with a specific verb and resource: 'Search for real estate properties in a geographic area.' It lists distinct filter criteria (zip code, subdivision, neighborhood, street name) and distinguishes from sibling tools like property_lookup and property_search_owner by emphasizing area-based search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides concrete example questions that clarify when to use the tool (e.g., 'what homes are in this zip code?'). It does not explicitly say when NOT to use or name alternative tools, but the examples and tool names imply the scope.

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

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

No annotations are provided, so the description carries the full burden. It discloses partial name matching and the return fields (addresses, values, property types, account numbers). It does not mention limitations like result limits or authentication, but it is adequate for a simple search tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is about 3-4 sentences, front-loads the purpose, provides concrete examples, and clearly states the return structure. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity of the tool (2 params, no output schema), the description covers purpose, matching behavior, return values, and example queries. It is complete for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%. The description does not add meaning beyond the schema for either parameter: owner_name's partial matching is already in the schema, and county is not elaborated. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Search' and resource 'real estate properties by owner name'. It provides examples that distinguish it from siblings like property_search_area and property_lookup.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives specific example questions (e.g., 'what properties does John Smith own?') that indicate appropriate usage. It does not explicitly exclude cases or name alternatives, but the context is clear.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It transparently states that the tool shows year-by-year data including specific value types and percentage change. It implies a read-only operation without destructive effects. The description adds context beyond what annotations would cover, though it could mention rate limits or data availability limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main purpose in the first sentence. It then lists example questions and the prerequisite. Every sentence adds value, though the list of example questions is somewhat verbose. It is appropriately structured and efficient for the information provided.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description partially compensates for the missing output schema by stating 'Shows year-by-year market value, land value, improvement value, and percentage change.' However, it does not specify the exact structure or field names of the output. Given that the tool has only 2 parameters and no output schema, the description could be more complete about the return format or data source.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage for both parameters (account_number and county), so the baseline is 3. The description adds only that account_number is required and can be obtained from property_lookup, but it does not provide additional meaning beyond the schema (e.g., format examples or default behavior for county).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool's purpose: 'Get property value history and tax assessment trends over multiple years.' It details specific output fields (market value, land value, improvement value, percentage change) and provides example questions. It also distinguishes from siblings by noting the prerequisite to use property_lookup first, clarifying the tool's role after account number retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear usage context with example questions and explicitly states the prerequisite: 'Requires account number (use property_lookup first to find it).' This helps the agent know when to use this tool versus siblings. However, it does not provide explicit when-not-to-use scenarios or alternative tool names for cases where the account number is already known.

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

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

With no annotations, the description explains that the tool filters a cached snapshot by listedOn because of a lack of public delta feeds. It does not disclose response format, pagination, or rate limits, but the filtering logic is transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first defines core function, second adds technical detail. Every phrase is necessary and front-loaded. No redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers what the tool does and why the filtering is needed, but with no output schema, it omits return format and pagination. For a tool with 3 parameters of moderate complexity, it is adequate but not fully comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 67% (source lacks description). The description adds context that source refers to 'four official lists' and explains the filtering reason, partially compensating. For since and limit, it adds no new meaning beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description specifies the verb 'return' and the resource 'entities added or updated since a given ISO date for a chosen source list.' It distinguishes from sibling tools like sanctions_get_entity (single entity) and sanctions_screen_entity (screening) by focusing on changes over time.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for monitoring updates but does not explicitly state when to use this tool instead of alternatives like sanctions_get_entity. It mentions that not all lists expose a delta feed, hinting at limitations, but lacks direct guidance on selection criteria.

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

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

No annotations are provided, so the description must disclose behavioral traits. It only implies a read operation ('Fetch') but does not mention rate limits, authentication, error handling, or what happens if the entity is not found. This is insufficient for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loads the action and resource, and contains no extraneous information. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple fetch tool with two parameters and no output schema, the description is adequate but minimal. It does not describe the output format or provide additional context such as pagination or field details, which could help an agent use the tool more effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50% (id has a description, source does not). The description adds context for id by stating it is source-specific and from screening, but does not elaborate on source values. It adds marginal value beyond the schema, but does not fully compensate for the missing description on source.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action 'Fetch a full record' and the resource 'entity ID and source', distinguishing it from screening tools like sanctions_screen_entity. It also notes that IDs are source-specific and obtained from screening results, providing clear purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description tells users to obtain IDs from a screening result, giving clear usage context. However, it does not explicitly state when not to use this tool or compare it with alternatives like sanctions_get_changes, leaving room for improvement.

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

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

No annotations are provided, and the description does not disclose behavioral traits such as read-only nature, authentication requirements, rate limits, or return format. The tool performs matching, but details are sparse.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is exceptionally concise with two sentences that efficiently convey purpose and a use case. Every word adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given four parameters, no output schema, and no annotations, the description is insufficiently complete. It lacks details on return values, matching behavior, error handling, or scenarios where the tool is inappropriate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers 100% of parameters, so the description does not need to add detail. However, it adds no extra meaning beyond the schema, such as expected address format or how sources relate to the threshold.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool matches a physical address against listed addresses and provides a use case (KYC/supplier vetting). However, it does not explicitly distinguish from sibling screening tools like sanctions_screen_entity or sanctions_screen_batch, leaving some ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a specific scenario (when name is generic but address is distinctive) but does not indicate when to avoid this tool or mention alternatives like sanctions_screen_entity for name-based screening. This provides partial guidance.

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

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

With no annotations, the description discloses key traits: output returns one block per input in order, and each name counts for billing. It doesn't cover rate limits or result details, but the billing info adds value beyond purpose.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, each essential: purpose, output format, billing. No redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers batch limit, output order, and billing, but lacks details on what a 'result block' contains. Given no output schema, more return format info would improve completeness, but current detail is adequate for basic use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds no parameter-specific meaning beyond what the schema already provides. The 'up to 50' limit is in both, so no extra insight.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (screen names), resource (names against sanctions lists), and batch limit (up to 50). It distinguishes from sibling tools like sanctions_screen_entity by specifying batch processing of names.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use (batch of names up to 50) but does not explicitly state when not to use or mention alternatives like sanctions_screen_entity or sanctions_screen_address for single entities.

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

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

Discloses return of matches with confidence scores, free tier limit (50/month) and standard rate ($0.05/screen). No annotations provided, so the description carries full burden; it lacks details on error handling or data freshness but covers cost and basic behavior well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences front-loaded with core purpose and lists, followed by additional detail on confidence and pricing. Extremely concise with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema and annotations, the description covers essential aspects: purpose, lists, confidence, pricing, and single-entity scope. It does not detail return format, but the core information is sufficient for a screening tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%; description adds context by naming the specific lists, explaining threshold as confidence 0..1 with default 0.85, and noting that limit defaults vary. This provides extra meaning beyond the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it screens a single name/entity against four major sanctions lists, differentiating from siblings like sanctions_screen_address and sanctions_screen_batch by specifying 'single name or entity' and listing the specific lists (OFAC SDN, EU consolidated, UN consolidated, BIS DPL).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies use for single entity screening, lists the specific sanctions lists used, and mentions pricing tiers (free/paid). However, it does not explicitly exclude use for addresses or batch screening, nor does it name alternative tools for those scenarios.

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

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

With no annotations provided, the description carries full burden. It implies a non-destructive search operation but does not disclose potential behavior such as error handling, pagination, rate limits, or authorization requirements. Basic transparency is present but lacks depth.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loaded with the core purpose, and the second sentence adds value by differentiating from a sibling. No unnecessary words or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple search tool with 4 parameters and no output schema, the description is reasonably complete. It covers the main purpose and key sibling differentiation. It could potentially mention more about the return format or default behaviors, but it is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All parameters have descriptions in the schema (100% coverage), and the description does not add significant meaning beyond the schema. It briefly reinforces that 'query' is an alias/AKA and 'lists' map to the sources parameter, but no additional parameter semantics are provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Search aliases / AKAs across selected lists' with a specific verb and resource, and explicitly distinguishes from the sibling 'sanctions_screen_entity' by noting that only alias fields are matched.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool over the sibling 'sanctions_screen_entity', citing the scenario where the primary listed name differs sharply from popular spelling. However, it does not mention other potential alternatives or when not to use the tool.

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

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

Without annotations, the description discloses that the tool is read-only ('no screening') and free. It specifies what it returns (counts and timestamps), but does not detail caching behavior or data freshness, though this is acceptable for a simple status tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise—two short sentences that immediately state the purpose and key behavioral traits. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the zero-parameter input and simple output (counts and timestamps), the description is fully sufficient. No output schema exists but the implied return is obvious.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has no parameters, so the description does not need to add parameter info. With 100% schema coverage (or rather no parameters to cover), the baseline is 4, and the description does not need to add meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool counts and provides last-update timestamps for all four lists in the cache. It explicitly says 'No screening is performed,' distinguishing it from other sanctions tools that perform screening or entity lookups.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It mentions the call is free and that no screening is performed, implying it's for cache status checks. However, it does not explicitly state when not to use it or direct to specific alternatives, though the context from sibling names helps.

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

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

With no annotations provided, the description carries the full burden. It discloses important behaviors: it returns exact tool names, logs every search to a roadmap database, and that high-frequency unmet queries jump the build queue. It also states it costs no credits. The description does not mention if it is read-only, but the side effect (logging) is non-destructive and transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is fairly long but front-loaded with the key message. Every sentence adds information (domain list, behavior, usage advice). It could be slightly more concise, but the level of detail is justified given the tool's role as a catalog guide.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple input schema (one required string), no output schema, and no annotations, the description covers all necessary aspects: purpose, usage guidelines, behavioral effects (logging, queueing), and what the output represents (exact tool names). It is complete for an agent to understand and invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'query' has a schema description that is 100% covered, but the tool description adds significant value by providing examples ('trucking safety', 'stock prices') and clarifying that the output includes exact tool names. This goes beyond what the schema alone provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as a guide to the entire data catalog, specifies its role as a first-call for tool selection or data availability queries, and lists the domains covered. It distinguishes itself from the many data-specific sibling tools by being a discovery meta-tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly advises 'Call this FIRST when you're unsure which tool to use' and provides example queries. It also states it costs no credits, encouraging free use. While it doesn't explicitly mention when not to use it, the context implies it's always the right starting point for data discovery.

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

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

No annotations are provided, so the description carries full burden. It describes the return fields but does not explicitly state that the operation is read-only or non-destructive. The behavior is implied but not fully disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loading the main purpose and then listing return fields and examples. Every sentence adds value with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (1 parameter, no output schema), the description fully covers purpose, parameter constraints, return fields, and usage examples. It is complete for the complexity level.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by specifying the allowed count (2-5) and providing example formats like 'AAPL,MSFT,GOOGL', going beyond the schema description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it compares 2-5 stocks side by side and lists the specific metrics returned. This distinguishes it from sibling tools like stock_quote (single stock) and stock_history (historical data).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit example queries like 'compare Apple and Microsoft' and 'tech stock comparison', indicating clear use cases. It does not explicitly state when not to use, but the context is sufficient.

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

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

No annotations are provided, so the description must carry the full disclosure burden. It mentions support for intraday and multi-year ranges and the type of data returned (OHLCV), but lacks details on behavioral aspects such as data source, timezone, trading day coverage, rate limits, or output structure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description consists of two sentences and a list of example queries, which is concise. However, the second sentence listing intervals partly duplicates schema enums, and the examples could be more compact. Still, it is well-structured and front-loaded with the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description should explain return values, which it does not. It covers purpose and usage examples but omits details like data rows, format, or pagination. For a 3-parameter tool, it is minimally adequate but incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents parameters. The description adds context via example queries but does not significantly elaborate on parameter semantics (e.g., meaning of period values, valid interval combinations). Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific verb 'Get' and resource 'historical stock price data', listing the exact fields (OHLCV). It distinguishes itself from siblings like stock_quote (current price) and stock_compare (comparison) by focusing on historical data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit example queries, indicating when to use the tool (e.g., 'how has AAPL performed this year?'). However, it does not mention when not to use it or explicitly name alternatives like stock_quote for real-time data, which would strengthen guidance.

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

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

Discloses it returns real-time data and lists return fields. No annotations provided, so description handles transparency adequately. Could add more detail on freshness or error handling, but sufficient for a simple read-only tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single paragraph with clear structure: action, return fields, examples. Efficient and front-loaded, but could be slightly more concise without losing examples.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, return fields, and usage examples. No output schema, so listing return fields is helpful. Could mention error cases or invalid symbols, but overall complete for a simple tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter 'symbol' with schema description coverage 100%. Description adds example tickers and usage context, but does not add significant meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it returns real-time stock price and market data, lists specific fields, and provides example queries. Distinguishes from siblings like stock_history and stock_compare.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides example queries to indicate typical use cases, but does not explicitly exclude alternatives or state when not to use it. Still, the examples give clear context for common scenarios.

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

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

Despite no annotations, the description discloses key behavioral traits: it returns real-time data, supports up to 10 symbols, and outputs a comparison table with specific fields. It implies a read-only operation, which is sufficient 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first defines function and limitation, second provides actionable examples. No redundant information; very concise and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple parameter, no output schema, and no annotations, the description fully covers what the tool does, what it returns, and how to use it. No gaps in information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage and already describes the 'symbols' parameter as comma-separated and max 10. The tool description repeats this but adds no new semantics beyond usage examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get', resource 'real-time stock prices for multiple stocks', and scope 'up to 10'. It includes specific examples like 'FAANG stocks' and 'compare tech stock prices', effectively distinguishing it from sibling tools like stock_quote.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases ('Use this for...') and query examples, making it clear when to use the tool. However, it does not explicitly mention when not to use it or suggest alternatives for single-stock queries.

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

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

No annotations provided, so description carries full burden. It states it returns recall details but does not explicitly state it is read-only or non-destructive. Adequate but leaves uncertainty about side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences plus a list of examples. Front-loaded with the main action and parameters. No redundant information. Every element earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (3 parameters, no output schema), the description covers purpose, parameters, and return content. It also clarifies scope ('all US vehicles'). Complete for the use case.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage with descriptions for each parameter. The description adds example values but no additional semantics beyond the schema. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it checks for safety recalls by year, make, and model. Differentiates from sibling tools like vin_decode by specifying NHTSA recall campaigns. Examples solidify the purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides specific example queries ('are there recalls on my car?', 'check recalls for 2020 Toyota Camry'). No explicit when-not-to-use, but the description implies it's the only tool for recalls. Could mention limitations like US-only.

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

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

No annotations are provided, so the description must cover behavioral traits. It discloses the tool is a read operation but lacks details on error handling, rate limits, or validation of VIN format beyond '17 characters'. It is minimally transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, listing returned fields and examples without redundancy. It could be slightly more concise but is well-structured and front-loaded with key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (1 required parameter, no output schema), the description covers the purpose, example usage, and scope. It lacks details on return structure but is sufficiently complete for an agent to understand when to use it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with one parameter 'vin' described as '17-character VIN'. The description does not add semantic information beyond what the schema provides, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly explains that the tool decodes a VIN to return full vehicle specifications, listing many specific data points. It differentiates from sibling tools like vehicle_recalls by focusing on VIN decoding.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides example queries and states 'Use this for...' but does not explicitly mention when not to use or alternative tools. However, no sibling tool directly competes, so the guidance is adequate.

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

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

No annotations provided, so description carries full burden. It states the returned fields and global scope (any city, zip code, place). Does not disclose potential errors, authentication, or rate limits. Adequately covers scope but lacks behavioral detail beyond returned data.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise, front-loaded with main purpose, then lists return fields, then example queries. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given simple tool with one parameter and no output schema, description is fairly complete: explains purpose, return fields, example usage, and scope. Could mention error handling for unknown locations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema already describes the 'location' parameter with examples (100% coverage). Description reinforces 'any city, zip code, or place name globally' but adds no new meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it gets current weather conditions, lists specific data points returned, and the verb 'Get' with resource 'current weather conditions' is specific. Distinguishes from sibling 'weather_forecast' by focus on current conditions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides example queries that imply current weather questions, but does not explicitly state when not to use (e.g., for forecasts) or name alternatives. Context is clear but lacks exclusion guidance.

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

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

With no annotations, the description covers output data but lacks additional behavioral context such as data source, update frequency, rate limits, or any potential limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is single paragraph but information-dense and front-loaded with purpose, then examples. It could be slightly more structured but is concise overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with 2 parameters and no output schema, the description lists return values adequately. However, it could specify units (e.g., temperature in Celsius/Fahrenheit) or time format.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage, so description does not add significant new meaning beyond the schema; it mentions 'default: 7' for days but that is implicit from schema's minimum/default.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides multi-day weather forecasts with specific data points (high/low temps, conditions, precipitation, etc.) and distinguishes itself from siblings like weather_current and air_quality through example queries.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes example queries that imply appropriate use cases (forecast planning), but does not explicitly state when not to use it (e.g., for current conditions vs. weather_current sibling).

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