aitrainer.work AI training jobs

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description nevertheless adds important provenance context: the practice questions are aitrainer.work's own and not a platform's real interview questions, which matters for how an agent presents the output. It says little about how the two optional inputs combine or what happens when one is omitted.

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 filler, and the caveat about question provenance is placed last where it reads as a qualifier. The opening leads with a content inventory rather than the action, which is slightly less front-loaded than ideal, but overall it is tightly written.

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?

An output schema exists, so return values need no explanation, and annotations carry the safety profile. With zero required parameters the remaining gap is only the behavior when role and/or platform are omitted, which is minor for a read-only 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?

Schema coverage is 100%, so both parameters are already fully documented with examples. The description only loosely implies the role/platform scoping ('for a role', 'how a platform hires') without clarifying whether either is optional, what the default is, or whether combining them narrows or broadens results.

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 the concrete content returned: hiring steps, interview format, aitrainer.work guides, and practice questions with strong-answer outlines for a role. That is clearly distinct from siblings like get_job, get_pay_stats, and search_jobs. It stops short of an explicit 'this is not X' routing statement, but the resource 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?

It gives two concrete usage contexts: 'explain what an interview involves' and 'run a mock interview.' That is enough for an agent to decide to call it, though no exclusion or alternative tool is named for cases where the agent actually wants the job posting (get_job) instead.

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 declare readOnly, idempotent, closed-world and non-destructive, so the safety profile is covered. The description adds the shape of the payload but says nothing about behavior beyond it — no handling of a missing slug/platform, no cross-platform behavior, no note on freshness of pay data despite mentioning 'its source'.

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?

A single front-loaded sentence with no filler. The trailing field enumeration is partly redundant with the output schema, but it still orients the agent quickly, so the slight redundancy is tolerable.

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 an output schema present, real annotations, and full parameter coverage, the description only needs to frame scope — which it does, including the useful 'pay and its source' nuance. The remaining gap is failure behavior for an unknown slug/platform.

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 both parameters explicitly state they come from search_jobs results, so the schema carries the semantics. The description adds nothing about slug/platform format or pairing rules, making this baseline territory.

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 retrieval action ('Full details of one role') and enumerates the payload (description, requirements, pay/source, hours, interview, countries), which is far more informative than the title alone. It implicitly distinguishes itself from search_jobs by retrieving a single item's full detail, but never names the sibling, and it drifts between 'role' and the tool's 'job' terminology.

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?

Usage is only implied: an agent can infer this is the follow-up call to search_jobs for a single record. There is no explicit when-to-use statement, no pointer to get_pay_stats or get_interview_prep as alternatives for the pay/interview facets it mentions, and no note that both platform and slug must come from a prior search.

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 declare readOnly, idempotent, closed-world, and non-destructive behavior, so the safety profile is covered. The description adds useful context beyond that: the data comes from hourly rates stated in listings, is broken down by platform, and reflects the latest monthly index — giving the agent freshness and provenance cues. It stops short of noting update cadence details or staleness caveats.

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?

One sentence, zero filler, with the resource and its two components (pay distribution and hiring outcomes) front-loaded. Nothing needs to be trimmed or reordered.

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 an output schema present and no parameters, the description only needs to convey what data is returned and its source, which it does. A brief note on what the hiring-outcomes component comprises would have closed the remaining gap.

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 takes zero parameters, so the baseline is 4. The description correctly implies a single unfiltered call covering all platforms, which is consistent with the empty 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 names the resource precisely: market pay statistics (median and quartiles of hourly rates from listings, by platform) plus hiring outcomes from the AI Training Pay Index. This clearly separates it from siblings like search_jobs, list_platforms, or get_interview_prep, though it leads with a noun phrase rather than a verb such as 'get' or 'returns'.

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?

There is no guidance on when to call this tool versus alternatives — nothing like 'use this for aggregate pay benchmarks rather than individual listings'. An agent must infer the use case entirely from the content description, and no conditions or exclusions are stated.

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 declare readOnlyHint, idempotentHint, closed-world and non-destructive, covering the safety profile. The description adds that each platform carries role counts, median pay, payout speed and availability, but since an output schema exists this largely restates structured return data rather than disclosing new behavior such as ordering, completeness, or refresh cadence.

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?

A single compact clause with zero wasted words, and the returned metrics are front-loaded. The only weakness is the missing leading verb, which costs a little immediate clarity.

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 zero parameters, full annotation coverage and an output schema, the description needs only to establish scope, which it does. A brief note on ordering (e.g., by role count) or on whether the list is exhaustive would close the remaining gap.

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 takes no parameters, so there is nothing to disambiguate and the baseline of 4 applies. The description correctly implies no filtering is needed.

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 is a noun phrase enumerating what the tool returns (platforms plus role counts, median pay, payout speed, availability), which makes the resource and its scope clear. It is distinguishable from siblings like search_jobs or get_pay_stats, though it never states an explicit verb such as 'list' or 'return'.

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?

Usage is only implied: 'Platforms we track' suggests this is the enumeration endpoint for the platform dimension, useful before filtering jobs or pay stats. There is no statement of when to call it versus the job/pay tools, and no prerequisites or exclusions.

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 establish readOnly/idempotent/non-destructive, and the description adds value beyond them: result ordering ('highest match first'), a cap of 10, per-result rationale, and a privacy guarantee ('Nothing about the person is stored'). It does not cover freshness of the job data or whether the ranking considers all open roles, but the additions are meaningful.

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: what it does, when to use it, what it returns. Nothing is redundant and the core 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?

With an output schema present, the description needn't detail return shape, and it still summarizes count and ordering. Parameters and annotations are well covered, leaving only minor gaps such as result freshness and whether ranking is strict or fuzzy on the profile fields.

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 every parameter is already documented in the schema. The description restates the matching dimensions and the USD currency of min_hourly_pay but adds no weighting, precedence, or fallback behavior beyond what the schema 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?

The description opens with a specific verb+resource ('Rank open AI training and AI evaluation roles') and enumerates the matching dimensions, so the tool's function is unambiguous. It does not explicitly name the sibling search_jobs to draw the contrast, relying instead on 'for one person' and 'by their profession, skills...' to imply the difference.

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?

'Use when the user describes their own background, e.g. "I am a nurse in Canada"' gives a clear trigger condition plus a concrete example. It stops short of naming alternatives (search_jobs) or stating when not to use it, so the routing is implied 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?

Annotations already cover the read-only, idempotent, non-destructive safety profile, and the description adds real behavioral context beyond them: AND-matching across keywords, most-relevant-first ordering with a documented tiebreak (newest first when no keyword), and the fields each result carries. It does not mention pagination beyond the limit parameter or any freshness/index staleness caveat.

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, front-loaded with capability, then return shape, then the one non-obvious calling rule. No filler and nothing repeated from the schema verbatim.

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?

Six optional parameters, an output schema that defines the return shape, and full annotation coverage mean little is left to explain. The description supplies exactly the missing pieces: what corpus is searched, how ranking works, and how to phrase a query.

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%, so baseline is 3; the description earns above that by explaining the non-obvious matching semantics of `query` ('Every keyword must appear'), which constrains how a caller should formulate terms. Other parameters (country, min_hourly_pay, domain) are 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 (search) and resource (open AI training / AI evaluation roles) with the domain spelled out (RLHF, model rating, expert data work) and the corpus scope (20+ platforms). An agent can distinguish it from get_job (single record), match_jobs_to_profile (profile-driven), and list_platforms without opening any 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 concrete query guidance ('use one to three words') and routes the agent to list_platforms for platform keys. It stops short of explicitly stating when to prefer match_jobs_to_profile over keyword search, so the alternative-selection rule is implied rather than stated.

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