Alaska Jobs

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 content (statistics and a citation sentence) but does not mention data freshness, update frequency, or any limitations. This is adequate but not rich.

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 main purpose ('Board-wide daily statistics') and then adds specifics. Every part earns its place, with no 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 the tool has no output schema, the description provides a solid overview of the return values (types of statistics and the citation sentence). It does not detail exact formatting, but for a 0-parameter tool, it is reasonably 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?

The tool has zero parameters, so the description is not required to explain parameter behavior. The baseline of 4 applies, and the description adds useful context about what the returned statistics cover.

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 that the tool provides board-wide daily statistics, listing specific metrics (open jobs, employers, new/closed this week, medians) and dimensions (category, region). This is distinct from sibling tools like get_employer or search_jobs, which focus on individual entities or searches.

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 context implies when to use this tool (when a summary of board-wide statistics is needed), and siblings focus on specific data lookups. However, it does not explicitly mention exclusions or alternatives, so it lacks an explicit 'when-not-to-use' statement.

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 for behavioral disclosure. It reveals the tool's primary behavior (returning profile fields) and the special behavior when name is omitted. It does not mention side effects, but as a profile/lookup tool, the read-only nature is implied and no contradictory hints exist.

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 short sentences, front-loaded with the core purpose and followed by a usage tip. Every word adds value—the list of contained data is specific, and the omit-name note is actionable 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 tool's simplicity (one optional param, no output schema), the description is complete. It lists the key return fields and explains both invocation modes (with and without name). This is sufficient for an agent to understand what to expect and when to use it, even without an output schema.

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% (the 'name' parameter is described). The description adds semantic value beyond the schema by explicitly stating that omitting 'name' lists employers, which explains the param's optionality and behavior. This goes beyond the baseline of simply repeating schema information.

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 a 'Profile of an Alaska public-sector employer' with specific data points ('current openings, hiring pace, typical posted pay'), and also explains the list mode when 'name' is omitted. This distinguishes it from sibling tools like get_job or get_salary_data, which focus on other entities.

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 for when to use the tool (to get an employer profile or list employers) but does not explicitly mention alternatives or exclusions relative to sibling tools. The 'Omit name' instruction provides conditional usage guidance, though no direct comparison to other tools is made.

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 of behavioral disclosure. It lists returned fields (description, salary, PCN, closing date, apply link), implying a read operation, but does not explicitly state read-only status, error handling, or any side effects. This is adequate but leaves gaps.

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

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

The description is one sentence that front-loads the purpose ('Full detail for one listing') and immediately provides the mechanism (by slug or URL) plus key return fields. Every word earns its place with 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?

For a simple one-parameter get operation, the description is fairly complete: it lists the return contents and input format. It lacks error-handling notes but compensates for the absent output schema by enumerating fields. Siblings are clear enough to disambiguate.

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 fully describes the 'slug' parameter, including the option for a full URL. The description repeats the same information and adds no new semantic detail, so per the baseline for high schema coverage, a 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 explicitly states 'Full detail for one listing by slug or alaskajobs.org URL', using a specific verb (get) and resource (job listing). It clearly distinguishes from siblings like search_jobs (search) and get_employer (employer-specific), so 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 description implies use when you have a specific job slug or URL and need full details. It gives clear context but does not explicitly mention alternatives or exclusions like 'use search_jobs to discover listings'. No when-not-to-use 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?

With no annotations, the description carries the full burden. It discloses the data source ('tracked postings'), scope ('Alaska public-sector roles'), and output nature (median and percentile). It could add specifics about behavior when no data is found, but overall it provides useful context beyond a simple label.

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 concise sentences, front-loaded with the main purpose and followed by a usage tip. Every word 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?

For a simple tool with one optional parameter and no output schema, the description covers the essential aspects: what it returns, data source, geographic scope, and parameter behavior. Minor gaps like error handling or output format exist but are not critical for a simple data lookup.

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?

While the schema describes 'role' with an example, the description adds key behavioral semantics: omitting the parameter lists all roles with data. This goes beyond the schema's simple type/description and clarifies the parameter's optional impact.

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 'median and percentile posted salaries for Alaska public-sector roles' computed from tracked postings. This specific verb+resource+scope distinguishes it from sibling tools like get_state_pay, which likely focuses on other pay 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 gives a concrete usage instruction ('Omit role to list all roles with data') but does not mention when to use this tool vs alternatives such as get_state_pay or search_jobs. The context is clear but no exclusions or alternative guidance 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 the full burden. It discloses the key behavior of applying AS 39.27.020 geographic differential, which is valuable context. However, it does not specify the return format (e.g., annual salary vs. hourly) or any error conditions.

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 official status and core function. Every phrase adds value, with no redundant or filler content.

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, the description covers the main inputs and the special differential, making it largely complete. However, without an output schema, it would benefit from stating the return format explicitly.

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 references all three parameters without adding significant new syntax or format details. It repeats the parameter names but does not clarify formats beyond the schema 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 tool returns official GG-schedule pay based on range, step, and duty station, with Alaska's statutory geographic differential. This specific terminology distinguishes it from siblings like get_salary_data, which may be broader or less specialized.

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 Alaska GG-schedule salary lookups, providing clear context that the tool is for obtaining pay with a range/step/duty station combination. However, it does not explicitly mention when not to use it or compare it to alternate tools like get_salary_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 must carry the burden. It discloses that all filters are optional, which is useful, and 'search' implies a safe read operation. However, it does not explain return format, pagination, sorting, or any other behavioral traits, leaving the agent partially in the dark about what to expect.

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 short sentences, front-loaded with the core purpose and a brief note on optionality. Every word earns its place, with no redundant or filler content.

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 no output schema and no annotations, so the description should explain what a search returns and how to use filters effectively. It only says 'Search ... listings' without describing result structure, default limit behavior, or filter combinations. Given five parameters (three with sparse schema info), this is incomplete for an agent to invoke correctly without guesswork.

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 only 40%, with query and remote having descriptions while limit, region, and category lack semantic explanations. The tool description merely states 'All filters optional,' which does not describe parameter meaning or usage. For enums like region and category, the description offers no clarification, failing to compensate for the low 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 'Search open Alaska government, legislative, campaign, policy, municipal, federal, and nonprofit job listings,' providing a specific verb, resource, and scope. This distinguishes it from sibling 'get' tools like get_job or get_salary_data, which target specific records or stats.

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 by saying 'Search open ... job listings,' but it does not explicitly mention when to use this tool versus alternatives like get_job for a specific job or get_salary_data for compensation data. The note 'All filters optional' offers some context but no direct when-to-use or when-not-to-use guidance.

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