fairsession

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

The description discloses the fallback to national data when the state sample has fewer than 25 disclosed postings and warns that advertised ranges are not settled offers. This adds meaningful behavioral and data-limitation context beyond the schema. It stops short of describing the exact response format, but the core behavior is transparent.

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

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

The description is a single dense sentence that front-loads the main comparison and then adds the fallback rule and caveat. There is no fluff, though the phrasing 'where a specific job offer sits' is slightly indirect.

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 and no output schema, the description provides substantial context: comparison logic, percentile basis, fallback threshold, and a data-quality caveat. It could mention return value shape or explicitly route to get_pay_range for raw ranges, but an agent has enough 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?

The input schema already provides full descriptions for all four parameters, including amount examples, so schema coverage is complete. The description reinforces the role/state/amount relationship but does not add parameter-level semantics beyond what the schema already conveys.

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 operation: compare a specific job offer to advertised pay and report its percentile. It names the resource (job offer vs live posting ranges) and role/state scope, which distinguishes it from list_employers. It does not explicitly differentiate from get_pay_range, but the core purpose is unambiguous.

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 use case is implied: call this when you have a specific offer amount and want to position it against market postings for the same role and state. The description gives no explicit when-not-to-use guidance and does not mention alternatives such as get_pay_range for raw range 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the metric formula, the weekly computation cadence, the data source, and the included output details such as posting counts and ranges by role. It also adds a trust-related guarantee that scores cannot be bought, which is more transparency than expected.

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: the first sentence defines the core metric, the second lists what is included, and the third offers a meaningful guarantee. Every sentence contributes non-redundant information and the main definition 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 simple one-parameter read-only tool with no output schema, the description is largely complete: it states the metric, source, update frequency, and return contents. An agent can correctly select and invoke the tool using only the employer name from the schema. Minor details such as not-found behavior are not specified, but this does not seriously impair usability.

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, employer, is fully documented in the schema with a description and example, giving 100% schema description coverage. The tool description adds no significant parameter-level detail beyond tying the score to an employer, which is adequate under the baseline rule.

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 resource and computation: an employer's Pay Transparency Score defined as the percentage of active postings that state pay, sourced from the employer's own public career board. It also distinguishes itself from sibling tools like get_pay_range and check_offer by focusing on an employer-level score rather than individual offers or ranges.

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

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

The description explains what the tool provides and how the score is computed, so an agent can infer when it is relevant. However, it never explicitly states when to prefer this tool over siblings or when not to use it. No alternatives or exclusion conditions 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?

With no annotations, the description carries the full burden, and it does well by disclosing the data source (live job postings), freshness (computed weekly), a negative constraint (never surveys), and what it returns. It also mentions honest labels for thin data, though it does not fully specify what those labels look like.

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 dense sentence with no filler. It front-loads the core purpose, then packs in the key behavioral details and return-value caveats without wasting 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 only two simple parameters, full schema coverage, and no output schema, the description is largely complete: it explains what is returned, how the data is computed, and how thin data is handled. A slightly more explicit note about the format of the returned ranges would make it fully 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%, so the parameters role and state are already well documented. The description adds useful context like 'healthcare role' and two-letter state codes, but it does not materially extend the schema's parameter explanations.

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 and resource: retrieving an advertised pay range for a healthcare role in a US state. It also differentiates the tool from siblings like check_offer and get_employer_score by focusing on market-wide pay ranges rather than individual offers or employer scoring.

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 for when to use this tool: whenever an agent needs current advertised pay ranges for a healthcare role in a state. It does not explicitly state when not to use it or name alternative tools, but the intended use case is unmistakable.

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 disclosure burden. It adds useful context by distinguishing tracked/scored employers from the not-yet-ingested pipeline, which is more than a bare list would provide. However, it omits details like pagination, ordering, result size limits, or whether pipeline entries include scores.

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 front-loads the main resource ('All employers') and then adds necessary scope qualifiers. Every phrase earns its place, with no repetition or 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?

For a simple no-parameter list tool, the description covers the main output categories: tracked employers with Transparency Scores and the pipeline. However, since there is no output schema, details about returned fields, pagination, or how pipeline entries are represented are left unspecified.

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 there is nothing for the description to clarify beyond what the empty schema shows. The baseline of 4 applies because parameter semantics are not a concern.

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 resource (employers tracked by Fair Session) and the scope: all tracked/scored employers plus the pipeline of identified-but-not-yet-ingested ones. It does not explicitly state the action verb 'list,' but the tool name and phrasing 'All employers...' make the purpose evident.

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 context but no guidance on when to choose this tool over siblings like get_employer_score or check_offer. It does not state what this tool is not for or when a more specific tool should be used.

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