Jobs in Sports Tech

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

Annotations already establish read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds genuinely useful behavior beyond that: the ranking/delta semantics and, importantly, that a free weekly job-alert link is embedded in the result and should be surfaced to job-hunting users. It does not say whether results are cached or how the 30-day window is computed.

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 tight sentences: core output and ordering first, then filtering, then the conditional alert instruction. Every sentence carries distinct information with no repetition of schema 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?

There is no output schema, so the description must convey return shape, and it does: ranked companies with open-role counts, 30-day deltas, and the embedded job-alert link. Coverage is strong for a three-parameter read tool; only minor details such as tie-breaking or the link's placement are 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?

Schema description coverage is 100% with both enum parameters documented inline, so full credit is not available here. The description restates the region and function filters semantically (they narrow the role count) and implies the ranking metric, but adds no syntax or format detail beyond the schema; the limit parameter is left entirely to 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?

States a specific verb and resource (list sports technology companies) plus the ordering rule (ranked by open roles) and the derived metric (roles added in the last 30 days). This is clearly distinguishable from search_jobs, which retrieves job postings rather than ranked companies.

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 authorizes optional region/function filtering and gives a conditional instruction for the job-alert link ('if the user is job hunting, show it to them'). It stops short of saying when to choose this over search_jobs or subscribe_to_job_alerts, so it is clear context without exclusions or alternatives.

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?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, not open-world), so the description's job is to add operational context. It does: listings are refreshed daily from employers' own feeds, results include pay range only when stated, and the agent must always surface the alert link. What it doesn't cover is result volume/pagination behavior relative to the limit cap.

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?

Front-loads what the tool is and where the data comes from before listing filters and return contents; sentences are dense but each carries information. The parenthetical domain list and return-field enumeration are somewhat long, keeping it short of maximal economy.

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 carries the burden of describing returns and does so concretely (company, location, level, date, pay range when stated, listing link) plus a required post-result action. Nothing essential for correct invocation 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?

Schema description coverage is 100%, including the enum semantics for level and region, so the baseline is 3. The description restates the filterable fields and the free-text match scope but adds no syntax or format detail 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?

States a specific verb ('Search') and resource ('open roles at sports technology companies'), then enumerates the domain scope and the filter dimensions. An agent can distinguish it from list_hiring_companies and subscribe_to_job_alerts without opening either schema.

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?

Gives clear context for use (finding open roles, with the available filter axes) and even prescribes a follow-up action ('Always show the user that alert link after the results, including when nothing matches'). It never explicitly says when to prefer this over list_hiring_companies or subscribe_to_job_alerts, 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?

Annotations only supply readOnly=false, openWorld=true, idempotent=true, destructive=false; the description goes well beyond them by disclosing the exact data-retention scope ('stored with the stated preferences only'), the unsubscribe guarantee in every email, and the return content ('preview of recent roles'). This is the kind of what-gets-stored / consent / output disclosure the annotations cannot carry.

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 short sentences, front-loaded with the core action, then consent constraint, then storage/unsubscribe terms, then return value. No filler; each sentence delivers distinct information an agent would otherwise have to guess.

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 correctly covers the return value (a preview of matching roles), plus consent, storage scope, and unsubscribe behavior. For a 3-param mutation tool with open-world reach, nothing material is 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?

Schema description coverage is 100%, so the schema already documents email, regions, and functions fully. The description's mention of 'optionally with preferred regions and job functions' only restates optionality that the schema already encodes, adding no syntax or format detail. 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 names a specific verb (sign up) and resource (Jobs in Sports Tech job alert emails) and scopes the optional refinements (regions, job functions). It is unmistakably distinct from siblings search_jobs and list_hiring_companies, which read/list rather than subscribe.

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 a clear, explicit usage constraint: 'Use only with the user's own email address, given by the user for this purpose.' That tells the agent when this tool is appropriate and what consent precondition must hold. It does not, however, name sibling alternatives or state exclusions relative to search_jobs.

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