Roleset — job postings index

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

No annotations provided, so the description carries full burden. It describes the output as 'every open posting' and accepted identifier formats, but does not explicitly state side effects (e.g., read-only) or permissions, leaving some ambiguity.

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 sentences with no redundancy; 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?

Description adequately conveys the tool's purpose and usage, and with output schema present, return values need not be detailed. However, it could mention parameter effects for full context.

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 33%. The description explains the identifier parameter (domain, slug, or name) but omits any detail about limit and include_description, which have minimal 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?

Clearly states the tool lists all open postings for a specific company, and explicitly distinguishes from search_jobs and hiring_signal siblings.

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 conditions: use when a company is known and roles are wanted; directs to alternatives for cross-company search and growth metrics.

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 burden. It discloses that the response includes the full description text and HTML and that the id comes from an earlier result, but it does not mention error behavior, authentication, rate limits, or confirm the read-only nature beyond the word 'Fetch'. 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 three sentences, front-loaded with the core purpose, followed by precise usage context and a source note for the parameter. Every sentence adds necessary information with no fluff or 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?

This is a simple single-parameter read tool with an output schema present. The description covers what it does, how to obtain the id, and when to use it relative to sibling tools. Nothing critical is missing for an agent to call 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 provides 100% coverage with the description 'Roleset job id (UUID) from an earlier result.' The tool description repeats this without adding new semantic details, so it stays at the baseline for full 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 states a specific action and resource: 'Fetch a single posting by its Roleset id, including the full description text and HTML.' The word 'single' and the focus on a unique identifier clearly distinguish this from sibling listing/search tools like search_jobs, job_changes, and company_jobs.

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 when to use this tool: 'Use after search_jobs or job_changes when you need the body of a specific posting'. It names the preceding sibling tools and gives concrete use cases, but does not explicitly state when not to use it (e.g., when only metadata is needed).

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 carries the full burden. It explicitly states that the tool 'returns' data, implying a read-only operation with no side effects. The nature of the output is fully disclosed, leaving no ambiguity about what the tool does.

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 with high information density. The first sentence explains the return value; the second provides use cases and a contrast to per-record tools. 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?

Given the moderate complexity (no output schema, but aggregate data), the description fully covers what the tool returns (department breakdown, counts, net change) and when to use it. It also implicitly differentiates from siblings by emphasizing aggregate vs. individual records.

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 'identifier' is fully described with three accepted forms: domain, ATS board slug, or company name. This covers 100% of the schema and gives the agent complete information needed to invoke the tool.

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 function: determining whether a company is hiring and where. It specifies the output—open roles by department, counts opened/closed over 7/30 days, and net change—which fully clarifies 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?

Explicit usage guidance is provided: use for sales and research questions about company growth, engineering expansion, and hiring slowdowns. The phrase 'One call, not per record' distinguishes it from sibling tools that return individual job postings.

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 available, the description carries the full behavioral disclosure burden, and it does so thoroughly. It explains the exactly-once, no-gaps/no-repeats cursor semantics, the 24-hour default, the event payload content (event type, timestamp, diff, job record), and the filtering behavior. This is rich, honest behavioral context beyond what any schema could infer.

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 dense but every sentence earns its place. It front-loads what the tool returns, then explains the polling protocol, filtering, payload details, and use cases in a logical order. No filler or repetition exists, and the length is justified by the tool's complexity.

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 11-parameter tool with no annotations and a real output schema, the description covers almost everything an agent needs: how to start polling, how to continue polling, what each event contains, how to filter, and when to use the tool. The output schema can handle detailed return shapes, so the description is complete without over-explaining.

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 55%, and the description adds meaningful semantics for key parameters: since is for the first poll, cursor is meant to be passed back from meta.next_cursor, and filters like department, country, remote, title, company, and ATS are mapped to fields. It does not explicitly explain limit or include_job, but those are relatively self-explanatory and the core polling parameters are well described.

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 what the tool does: it reports events that 'opened, changed, closed or reopened in the index.' It also states that this is a polling tool, which distinguishes it from siblings like search_jobs or get_job, and it names the event types available. The purpose is specific and actionable, not a restatement of the title.

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 strong usage context: 'This is the tool to poll,' explains the since/cursor flow, shows how to filter, and lists concrete use cases like tracking a market or syncing a store. It does not explicitly name sibling tools or state when not to use them, but the polling guidance is clear enough for an agent to select this tool appropriately.

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 does so well. It discloses pagination via meta.next_cursor, the record fields included, the fact that descriptions are omitted unless include_description is set, and that polling in a loop is inappropriate.

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 explains return shape, the one caveat about descriptions, and the sibling alternative. Every sentence earns its place and there is no redundant restating of the tool title.

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 14 parameters, no annotations, and an output schema, the description still gives the agent everything essential: when to call it, what results look like, how pagination works, the expensive-field caveat, and when to choose a different tool. The parameter details are largely covered by the input 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 79%, so the schema handles most parameter meaning. The description adds real value by explaining the include_description trade-off ('descriptions are omitted because they are large'), which is not clear from the schema's bare boolean field 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?

States a specific verb and resource: 'Search the index of open job postings.' It also frames the use case ('one-off question about the job market') and distinguishes itself from job_changes, so an agent can tell it apart from the closest sibling 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?

Explicitly says when to use it: for one-off job market questions such as who is hiring, what is open in a city or country, and which companies have remote positions. It also names the alternative for change detection ('use job_changes instead') and warns against polling this tool in a loop.

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 implies a read-only operation (gathering stats) and mentions it is 'Free', which hints at no side effects or limitations. However, it does not explicitly state that it never modifies data, but the nature of the tool makes this clear enough.

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, using only two sentences to convey purpose, content, and usage. No unnecessary words or 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 that the output schema exists and no parameters are present, the description provides all necessary context: it lists the specific metrics that will be returned and explains when to use it. It is self-contained and leaves no obvious 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 tool has zero parameters, so there is nothing to explain. The description adds no parameter-specific meaning, but no such meaning is needed. The schema coverage is automatically complete.

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 size and freshness metrics for the entire index, enumerating specific data points (companies, boards per ATS, postings, events, crawl time). This distinguishes it from sibling tools that focus on individual companies 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?

It explicitly tells when to use the tool: to inform users about coverage behind an answer or to verify index freshness before relying on it. The word 'Free' also signals it has no cost or restrictions, adding practical guidance.

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