SpinHire iGaming Jobs

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 adds some context: the output is markdown and includes open jobs. Yet it doesn't mention side effects, permissions, rate limits, or other behaviors, though for a GET-like tool this is a modest gap.

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 sentences, both essential: the first states the tool's output and scope, the second provides the input source. No redundant or filler content, and the key purpose 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 single-parameter tool with an output schema present, the description covers the core information: what the tool returns, in what format, and where to obtain the required slug. It doesn't address error cases or authentication, but those are not critical for this simple retrieval tool's correct usage.

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 'slug' with zero description coverage, so the description must compensate. It does so by explaining that slugs come from search_jobs results and identifies the exact field name (company_slug), adding meaningful semantic context 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 the tool retrieves an employer profile as markdown along with its open jobs. This specifies the verb and resource, and the resource type (company/employer) differentiates it from siblings like get_job and get_profession, though it doesn't explicitly name any sibling.

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 usage context by stating that slugs come from search_jobs results (company_slug), implying a workflow where this tool is used after search_jobs. However, it doesn't explicitly state when to choose this over alternatives or mention any exclusions, so the guidance is implicit rather than explicit.

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 output format (markdown) and content, but does not mention whether the operation is read-only, what happens if the job_id does not exist, or any authentication requirements. It does not contradict any annotations since none are provided, but it lacks depth on error handling or side effects.

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 immediately states the tool's output ('Full job posting as markdown') and lists the key fields. It is front-loaded, concise, and every word earns its place without any 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 get-by-id tool, the description covers the return content adequately, and since an output schema exists, return value structure is not required. However, it lacks usage context, parameter clarification, and any mention of behavior when the job is not found. It is minimally sufficient but leaves room for ambiguity.

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. However, it never mentions the job_id parameter, its format, or how to obtain it. The agent must infer from the tool name and parameter name that an integer ID is needed, but no additional guidance is given.

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 purpose: retrieving a full job posting as markdown, and lists the specific fields included (title, company, location, salary, description, source link). This distinguishes it from sibling tools like get_company or search_jobs by focusing on a single job's complete 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?

No guidance is provided on when to use this tool versus alternatives such as search_jobs, which would be used to find jobs before retrieving details. There is no mention of prerequisites, such as having a valid job_id obtained from a search, or when to choose this over other get_* tools.

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 must disclose behavior. It states the output is a markdown card and lists its contents, which is sufficient for a read-only lookup. However, it does not mention error handling, language behavior, or any side effects, though these are minor for a get operation.

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 sentences total, with the content list front-loaded and the usage hint second. No wasted words; 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?

Given that an output schema exists, the description need not detail return values. It covers the core purpose, content, and how to obtain required arguments. The only gap is the undocumented 'language' parameter, which slightly lowers completeness.

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 must explain parameters. It gives a hint about 'slug' via the list_professions reference and examples, but it never mentions the 'language' parameter or its default behavior. This leaves a significant gap for a parameter that the schema does not document.

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 clear verb ('get'), a specific resource ('profession card'), and enumerates its contents (responsibilities, KPIs, skills, tools, salary bands, career path, FAQ). It also references the sibling tool list_professions, making it easy to distinguish from get_company and get_job.

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 the agent to use list_professions to find valid slugs and gives concrete examples. It does not mention exclusions or alternative usage, but the workflow is clear and no other tool would serve the same 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 burden and does provide meaningful behavior: it returns 35 professions, the specific data fields included, and a language choice of 'en' or 'ru'. It does not explicitly claim read-only status or mention limitations, but the 'list' semantics and exact count 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?

Two compact sentences front-load the core purpose and then specify the language parameter. No filler or redundant wording.

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-optional-parameter list tool with an output schema present, the description is complete: it states the data scope, output fields, and parameter values. Nothing needed to invoke it correctly 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 only defines language as a string with default 'en'; the description adds the allowed values 'en' or 'ru', compensating for the schema's lack of documentation. It doesn't explicitly say what the language affects, but the value constraint is useful.

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 resource (iGaming professions) and states exact output contents: 35 professions with department, salary band, and live job count. This clearly distinguishes it from singular lookup tools like get_profession and job search tools.

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 tool is for retrieving a fixed list of professions and their salary/job data, but it does not explicitly state when to choose it over get_profession, search_jobs, or market_stats. No alternatives or exclusions 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?

No annotations are provided, so the description carries the burden. It discloses the data scope (monthly archive, daily snapshots) but doesn't mention whether this is a read-only operation, how far back the archive goes, or what the response format looks like. For a 0-parameter tool, the description is reasonably transparent about what data is returned.

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 core resource (monthly archive) and then lists contents. It is concise and structured, though the phrase 'and daily snapshots' is slightly tacked on at the end.

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 0-parameter tool with no output schema, the description is fairly complete: it tells the agent what data is available. It could be improved by noting the time range or that it's a read-only historical view, but nothing critical is missing for invoking the 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?

There are 0 parameters, so the schema provides no parameter documentation. The description compensates by explaining what the tool returns, which is the only semantic content an agent needs for a parameterless call. Baseline 4 for 0 params 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 states a specific resource (monthly archive of the iGaming job market) and what it contains (open jobs at month end, companies, new postings, daily snapshots). It is clear enough to distinguish from siblings like market_stats, though it doesn't explicitly name a sibling.

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 for historical market data, but it doesn't explicitly say when to use it vs market_stats or search_jobs. The context signals show 0 parameters, so the tool likely returns a broad archive; the description gives some context but no explicit 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?

No annotations are provided, so the description carries the burden. It discloses the data source URL and the scope of the data (current snapshot), which is useful. However, it doesn't disclose whether the data is cached, how fresh it is, or whether it reflects a live scrape versus a precomputed snapshot.

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 compact sentence that front-loads the core value ('iGaming job market right now') and then lists the specific data dimensions. The source URL is useful but slightly tacked on; overall it's efficient and scannable.

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 parameterless tool with no output schema, the description covers the main content dimensions and the source. It doesn't describe the return format, but with no parameters and no output schema, the description is reasonably complete for an agent deciding whether to call it.

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 no schema to compensate for. The description appropriately explains what the returned market data contains, which is the only meaningful semantic content needed for a parameterless 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 provides iGaming job market statistics: open jobs, new this week, companies hiring, and breakdowns by department, country, working language, and work format. It names a specific resource (the market page) and distinguishes it from sibling tools like get_company or get_job, though it doesn't explicitly name a sibling alternative.

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 tool to use for current market-level stats, and the sibling list includes market_history, which suggests a temporal alternative. However, it doesn't explicitly state when to use this tool versus market_history or search_jobs, leaving the agent to infer the distinction.

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 of behavioral disclosure. It states that query matches title/company/tags, lists valid department values, specifies country as English name or 'Remote', and work_format as office/remote/hybrid. It also notes the return limit of up to 50 per page. However, it omits details like pagination defaults, how filters combine (AND/OR), whether the query is fuzzy or exact, and any auth or side-effect information. It covers the basics but not the full behavioral profile.

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 paragraph that front-loads the purpose and then details the parameters. It uses semicolons to separate parameter guidance, which is efficient. The department list is long but necessary to give valid values. There is no fluff; every sentence adds 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?

Given there is no output schema and no annotations, the description should explain the return value and any operational nuances. It only says 'Returns up to 50 per page,' which is minimal. It does not describe the result structure, error handling, sorting, or how page/limit interact with the max of 50. For a tool with six parameters and no other structured documentation, this is incomplete but not severely lacking.

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 explain all parameters. It explicitly covers query, department (with a list of valid values), country, and work_format, and implies page/limit via the 'up to 50 per page' statement. It does not explain the default values of page and limit or that limit can exceed 50, which is a gap. Nevertheless, it adds substantial meaning beyond the bare 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 ('Search') and resource ('live iGaming jobs') with clear scope. It distinguishes itself from siblings like get_job (which presumably fetches a single job) and list_professions (which lists professions) by focusing on a search over jobs with multiple filters. The examples and enumerated values make the tool's function unmistakable.

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 a clear context: this is a search tool for iGaming jobs, and it explains what each filter does. It does not explicitly name alternatives or state when NOT to use it, but the sibling names (get_job, get_company) imply that this is for discovery while those are for specific entities. This is sufficient but not as explicit as it could be.

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