WARN Firehose — Labor Market Intelligence

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

With no annotations provided, the description carries the full burden. It usefully discloses the return content (tier and access level), but it does not describe error behavior, security considerations around the API key, or whether any side effects occur. This is adequate but not richly 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 short and front-loaded, stating the action and return value first, followed by a compact Args section. Every sentence contributes useful information, and there is no filler or repetition.

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 single-parameter authentication tool, the description covers what it does, what it returns, and the parameter format. Since there is no output schema, the return description is helpful, though it does not mention error responses or how to handle an invalid key; these are minor gaps for this low-complexity 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 schema only defines api_key as a generic string with 0% description coverage. The description compensates by identifying the key as the LaborCurrent API key and specifying its expected format ('starts with wf_'), adding meaning the schema alone does not provide.

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 states a specific action ('Authenticate'), the resource ('LaborCurrent API key'), and what it returns ('your tier and access level'). This clearly distinguishes it from the sibling data-retrieval tools, which all focus on records, layoffs, market data, and statistics.

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 this is the initial authentication step, but it never explicitly says 'use this before other tools' or contrasts it with alternatives. There is no exclusion guidance, so the usage context is only implied by the tool's name and purpose.

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 return limit ('up to 500 records'), full field details, and API key requirement. However, it does not explicitly state whether the operation is read-only, nor does it cover rate limits, error handling, or pagination, leaving some behavioral gaps.

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

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

The description is efficiently structured with a summary, an args list, and an alternative note. It front-loads the purpose and then details parameters. Slightly verbose with the API key instruction, but each sentence 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 5 parameters and no output schema, the description covers purpose, filters, limits, and prerequisites. It could mention the return format (e.g., JSON) and potential error scenarios, but the essentials are present.

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

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

Schema description coverage is 0%, so the description is the only source of parameter meaning. It explains each parameter: state as a 2-letter code, company as partial match, days with max 730, limit with max 500, and api_key with tier requirement. This fully compensates for the schema's lack of 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 'Export WARN records in bulk' with a specific verb and resource. It distinguishes itself from sibling tools like search_layoffs (search) and get_recent_layoffs (recent data) by focusing on bulk export with full field details.

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 explicitly directs users to the REST API for CSV/Parquet downloads, indicating when not to use this tool. It also states the Pro tier requirement upfront, giving clear prerequisites and an alternative path.

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 transparency burden. It discloses partial match behavior and the optional api_key for higher rate limits. However, it doesn't mention pagination or response shaping, but as a read-only get, 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?

Two front-loaded sentences plus a compact Args list. No fluff, every sentence earns its place. The format is scannable and clear.

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

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

Given the output schema exists, return values needn't be described. The tool is simple (2 params, no nesting) and the description covers purpose and parameters well. It could note when to choose this over search_layoffs, but overall it's complete enough.

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 descriptions are completely absent (0% coverage), but the description's Args section explicitly explains both parameters: company (partial match) and api_key (optional, higher rate limits). This fully compensates for the missing schema metadata.

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 uses a specific verb ('Get') and resource ('WARN Act layoff notices') scoped to a specific company, and notes partial match support. This clearly differentiates it from sibling tools like search_layoffs (broader search) and get_recent_layoffs (time-based).

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 company-specific lookups but does not explicitly state when not to use it or mention alternatives. It would benefit from a note like 'For broader searches, use search_layoffs instead.'

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 burden. It discloses the returned data and mentions api_key for higher rate limits, which is useful. However, it doesn't explicitly state whether the call is read-only, whether authentication is required for basic access, or any error/limitation 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 compact and well-structured, leading with the core action and using a bulleted list to summarize outputs. No unnecessary words or repetition.

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 complex snapshot tool, the description covers the main purpose, outputs, and the only parameter. Output schema exists, so extra return details are helpful but not required. It could mention limitations like data freshness, but it's sufficient for basic invocation.

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

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

The schema provides only a bare optional string param with no description. The description explains 'api_key: Optional API key for higher rate limits', adding meaningful practical purpose 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 'Get a single-call market snapshot across all 6 datasets' and enumerates the return categories. This distinctly differentiates it from siblings like get_company_layoffs or get_recent_layoffs, which have narrower scope.

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 when you need a broad cross-dataset overview in one call, but it does not explicitly state when not to use it or name alternative tools. There's no clear exclusion 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?

There are no annotations, so the description must carry the safety and behavior disclosure. It says 'Return/Returns' and lists the response contents, which implies a read-only operation. However, it does not state whether authentication is required or clarify that the tool only provides signup links rather than performing the upgrade itself.

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 with no filler. The first sentence states the core capability, the second gives usage triggers, and the third specifies the returned data and its usefulness. The most important information is front-loaded.

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

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

For a zero-argument, side-effect-free lookup, the description covers what it returns and when to call it. It lacks an output format example, but the enumerated fields—prices, daily call limits, datasets, signup links—are sufficient for an agent to 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 tool has zero parameters, so the input schema already fully communicates that no arguments are needed. The description adds no parameter-specific detail, but none is required; the 0-parameter baseline 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 opens with a specific verb and resource: 'Return LaborCurrent pricing tiers, included tools, and signup URLs.' This unambiguously identifies the tool's function and clearly distinguishes it from the layoff/data siblings, none of which concern pricing or account plans.

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 gives explicit trigger conditions: 'when the user asks about pricing, hits a rate limit, or wants to upgrade.' It does not name exclusions or alternatives, but none of the sibling tools appear to cover pricing, so the guidance is clear and actionable.

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 some behavioral burden. It discloses the optional API key for higher rate limits, which is useful. However, it omits other behavioral details such as output format, sorting, pagination, or error handling. The read-only nature is implied by 'Get' 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?

The description is appropriately sized: a one-line summary followed by a clear, structured Args list. Each parameter gets a concise explanation. No redundant or irrelevant 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 the tool's simplicity and the existence of an output schema, the description adequately covers purpose and parameters. It does not explain return values (not needed due to output schema) but lacks some usage context and behavioral nuance that would make it fully complete for agent invocation.

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

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

Schema description coverage is 0%, but the description explains every parameter: days (look back period), state (optional filter), limit (max results), and api_key (rate limits). This fully compensates for the schema's lack of descriptions and adds meaningful semantics.

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 'Get the most recent WARN Act layoff notices' with a specific verb and resource. While it doesn't explicitly differentiate from sibling tools like get_company_layoffs or search_layoffs, the focus on 'recent' and 'WARN Act' provides enough specificity for most use cases.

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?

There is no explicit guidance on when to use this tool versus alternatives like search_layoffs or get_company_layoffs. The description implies it's for recent notices but doesn't mention exclusions, prerequisites, or 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?

With no annotations, the description carries the behavioral burden. It discloses the scoring thresholds, the data sources combined, and the authentication/tier requirement. The verb 'get' signals a read-only operation, though it doesn't explicitly state rate limits or absence of side effects; the output schema covers return shape.

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 purpose, then gives scoring levels, access requirements, and compact parameter bullets. It is longer than minimal but every section earns its place by conveying needed invocation details without 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 output schema exists, return-value documentation isn't needed. The description fully covers the composite score meaning, thresholds, data sources, access tier, and all four parameters, making it self-sufficient for an agent to 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 coverage is 0%, so the Args section must compensate and does. It explains state as an optional 2-letter filter, min_score as a risk threshold, limit with max 50, and api_key with tier requirement. This meaningfully adds semantics beyond the raw schema fields.

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

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

The description opens with a specific verb and resource: 'Get companies ranked by composite distress signal across all datasets.' It then enumerates the exact data sources (WARN, SEC, bankruptcy, H-1B denial rates), which clearly differentiates it from sibling tools like get_company_layoffs or get_market_pulse that focus on narrower signals.

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 context: use this tool when you need a composite cross-dataset risk score, not a single-signal lookup. It doesn't explicitly name alternatives or exclusions, but the 'across all datasets' phrase and scoring-level explanation make the intended use obvious. It also discloses the Starter-tier requirement.

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 behavioral burden. It clearly discloses what the tool returns, that it combines six datasets, that Pro tier is required, and where to obtain the API key. It does not discuss rate limits or error behavior, but the main operational requirements are transparent.

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

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

The description is compact and well organized, with a one-sentence purpose, a returns summary, access requirements, and a short Args list. It is slightly redundant in mentioning the Pro tier twice, but overall every sentence contributes useful 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 moderately complex tool with no annotations and an output schema available, the description is sufficiently complete: it names all six datasets, the composite score, the required tier, and both arguments. It could add explicit differentiation from sibling tools, but nothing essential for invoking the tool is missing.

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

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

The schema has 0% description coverage, so the description must compensate, and it does: it documents state_code as a two-letter abbreviation with examples (CA, TX, NY), and api_key as a Pro-tier LaborCurrent API key. This gives the agent full parameter-level guidance beyond the raw 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 states a specific verb ('Get') and a specific resource: a unified state profile combining all 6 datasets. It lists the exact datasets (WARN, LCA, H-1B, DOL, bankruptcy, JOLTS) and a composite distress score, which clearly distinguishes it from siblings like get_state_summary or get_market_pulse.

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 conveys clear context for use: it is for a US state and unifies six datasets. However, it does not explicitly say when to prefer this tool over related siblings such as get_state_summary, nor does it state when not to use it, leaving usage guidance mostly implied.

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 behavioral burden. It implies a read operation ('Get') and mentions api_key for rate limits, but does not state whether any data is modified, whether authentication is required, or how errors (invalid state) are handled.

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 plus a clean parameter list. It is front-loaded with the purpose and every line adds value. 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?

The tool is simple (2 params, output schema exists), so the description is sufficient for invocation. It does not elaborate on return value details (covered by output schema) but lacks any notes on limitations or edge cases that might be relevant for a complete picture.

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

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

Schema coverage is 0%, so the description compensates by explaining both parameters: state as a two-letter code with examples, and api_key as optional for higher rate limits. This adds meaningful context beyond the raw 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 uses a specific verb ('Get') and identifies a concrete resource ('a summary of WARN Act layoff data') scoped to 'a specific US state'. This clearly differentiates from general query tools like search_layoffs.

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 siblings like get_state_intelligence or search_layoffs. The description does not mention any exclusions or alternate tool recommendations.

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. The verb 'Get' implies a read operation, but nothing is said about authentication requirements, whether the api_key is needed, rate limits, or what general statistics means. This is minimal inferred behavior rather than explicit disclosure.

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 concise sentence with no filler or redundant phrasing. It front-loads the main purpose, though the phrase 'overall statistics' is vague enough that it does not do much work beyond naming the tool's domain.

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

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

The tool has an output schema, so return values may be documented there, but the description still omits important context such as authentication requirements, what kind of statistics are included, and how this differs from the many sibling tools. Without annotations, this is under-specified for an agent selecting among many get_* options.

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

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

Schema description coverage is 0%, and the description does not mention the api_key parameter at all. The parameter name and default null give some semantic hint, but the description adds no value beyond the input schema regarding how or when the API key should be supplied.

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 states a clear verb ('Get') and resource ('overall statistics about the LaborCurrent database'), which distinguishes it from the more specialized sibling tools like get_company_layoffs or get_market_pulse. It does not explicitly name a sibling or scope edge case, but the word 'overall' provides enough differentiation.

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?

There is no guidance on when to choose get_stats instead of the many more specific get_* or search_* tools. No exclusions or alternative routing are provided, so the agent must infer that this is for high-level database statistics.

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 behavioral disclosure burden. It does add meaningful context: authentication is required, a Starter tier is needed, and results are cross-referenced with LCA visa roles. However, it does not mention rate limits, pagination, error behavior, or explicitly confirm a read-only nature, leaving some behavioral gaps.

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

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

The description is front-loaded with the core purpose and remains scannable with a clear Args block. Minor redundancy exists: authentication is stated twice (once in prose and once in the api_key line), and the 'talent' concept appears in both the first and second sentence. Overall it is still compact and well organized.

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 moderate read-style API with four optional parameters and an existing output schema, the description covers the core action, use case, authentication, and all parameter semantics. It does not provide examples or edge-case behavior, but nothing essential appears to be missing for an agent to call 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 coverage is 0%, and the Args section fully compensates by explaining all four parameters: state is a 2-letter code, days has a default and meaning, limit has a default and max of 50, and api_key requires Starter tier. This is meaningfully richer than the bare input schema, which only provides types and defaults.

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

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

The description opens with a specific verb and resource: 'Find available talent from recent layoffs, cross-referenced with LCA visa roles.' It further clarifies the output as occupations/skills per laid-off company, which distinguishes it from siblings like get_recent_layoffs and get_company_layoffs. This is not a tautology or vague restatement.

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 gives clear use context: 'Useful for recruiters targeting skilled workers from recently laid-off companies.' It also communicates the Starter tier requirement and where to get an API key. However, it does not explicitly name alternatives or state when not to use this tool, so it stops short of a 5.

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 transparency burden. It usefully discloses that an API key can raise rate limits and that limit has default/max bounds. However, it does not mention whether results are paginated, whether authentication is required, or any side effects (though 'search' implies non-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 compact and well-structured: a one-sentence purpose followed by a bullet-style Args list. Every line adds value and there is no filler.

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 input semantics and includes practical constraints (default/max, examples, API key purpose). An output schema exists, so return values are documented elsewhere. It lacks only an explicit tie to sibling tools and authentication prerequisites, which would make it fully self-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?

The input schema has no descriptions for any parameters (0% coverage), but the description's Args list fully explains all four parameters: query (search term), state (2-letter filter), limit (default/max), and api_key (rate limits). This directly compensates for the schema gap.

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

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

The description opens with a specific verb and resource: 'Search WARN Act layoff notices by company name, city, or keyword.' This clearly defines what the tool does and differentiates it from siblings like get_company_layoffs (targeted company lookup) and get_recent_layoffs (time-based listing).

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 conveys clear context for use: it searches notices by free-text terms with optional state and limit filters. However, it does not explicitly contrast with sibling tools or state when not to use it, such as when querying a single known company (for which get_company_layoffs might be better).

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