AITraining.jobs

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

Annotations already establish readOnly, idempotent, openWorld, and non-destructive behavior, so the safety profile is covered. The description adds meaningful semantics beyond that: the 7-day recency window, the grouping dimensions, and the optional country scoping. It doesn't discuss data freshness or rate limits, keeping it just short of 5.

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 that leads with the three core counts before the optional scoping detail. The nested parenthetical is slightly dense but every clause carries information; nothing is padding.

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, read-only aggregation tool with no output schema, the description conveys the return categories (counts, last-7-day count, breakdowns by work kind and platform) and the optional filter, which is enough to call it correctly. Exact response field names are unspecified, leaving a minor 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?

With a single parameter and 100% schema description coverage, the schema already documents the ISO 3166-1 alpha-2 country code and its filtering semantics. The description's restatement ('optionally scoped to roles open to applicants in one country') adds no syntax or format detail beyond it, so 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?

Names a specific resource (AI training job market) and enumerates exactly what the tool reports: total open roles, roles listed in the last 7 days, and breakdowns by kind of work and platform. It also distinguishes itself from the sibling search_ai_training_jobs by framing itself as the aggregate view whose platform slugs feed search.

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?

Implies the workflow clearly: call this to get market aggregates and platform slugs, then use those slugs in search_ai_training_jobs. However, it never explicitly states when to prefer get_ai_training_pay or when-not to use this tool, so full routing guidance is only partial.

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, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds genuinely non-obvious behavior: figures describe open-role advertised rates, and a rate is suppressed unless at least three platforms advertise it, plus each figure carries its own sample count. This data-threshold disclosure is exactly the kind of context annotations cannot convey.

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, no filler. The payload (what pays what, per kind of work) leads, and the qualifying details (percentile definition, sample counts, three-platform floor) follow. Every clause earns its place.

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 carry the return-shape burden, and it does: it names the central statistic, the dispersion measure, the provenance counts, and the suppression rule. An agent knows what it will receive and how to caveat it without further documentation.

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 takes no parameters (additionalProperties: false), so there is nothing for the description to disambiguate; the baseline for a zero-parameter tool is 4. No parameter meaning is missing.

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 (get) and resource (AI training pay), scopes it to 'by kind of work,' and enumerates the exact figures returned (median, 25th–75th percentile, sample counts). It is clearly distinct from search_ai_training_jobs in concept, but never names or contrasts the siblings explicitly, so it stops short of a 5.

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 it should call this for current hourly pay benchmarks, but there is no explicit when-to-use, no precondition, and no routing away from get_ai_training_market or search_ai_training_jobs. With zero parameters the invocation is trivial, which keeps this at a viable 3 rather than lower.

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, openWorldHint and destructiveHint=false, so the safety profile is fully covered structurally. The description adds useful scoping context (pay figures are 'advertised now', legitimacy is a 'researched review', ratings only 'where one exists'), but says nothing about behavior for unknown platforms or data freshness beyond the word 'now'.

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 that leads with the core resource and then enumerates return contents, with no filler or marketing fluff. The heavy nested parentheticals make it dense and slightly hard to scan, which keeps it below a 5.

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?

One required parameter, no output schema, and the description effectively serves as the return-value contract by listing roles/pay, legitimacy, acceptance, fit and page link. What remains unstated is the failure mode for an unknown platform and whether pay data is live or cached, but the definition is otherwise sufficient to call 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?

With a single parameter and 100% schema description coverage, the schema already documents the 'platform' field including examples and the name-or-slug flexibility. The description corroborates by listing real platform names, but adds no format or matching rules (e.g., whether matching is case-insensitive) beyond the schema, so 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?

Names a specific resource (a single AI training platform profile) and enumerates what the response covers: open roles and advertised pay, legitimacy review with risk and payment details, acceptance requirements, fit, and a link. It is clear what the tool returns, but it never contrasts itself with siblings such as get_ai_training_pay or get_ai_training_market, so the overlap with pay-focused siblings is left for the agent to resolve.

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 phrase 'One AI training platform' implicitly signals single-platform lookup versus the market/pay siblings, and 'for example Mercor, Outlier...' hints at accepted inputs. However, there is no explicit when-to-use, no exclusions, and no named alternative such as 'for cross-platform pay comparisons use get_ai_training_pay'; usage must be inferred from sibling names alone.

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, non-destructive and openWorld, so safety is covered; the description adds genuinely useful behavioral content by enumerating what the payload contains (counts by field, pay rates, pricing, briefing page URL). It omits freshness/caching of the "live counts" and result size, but for a single-parameter read this is beyond the annotation baseline.

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?

It front-loads audience marketing copy ("For teams building AI who need expert human data") rather than the action, then packs one very long sentence with a parenthetical job-title list and a six-item clause chain. The information is relevant but padded — the roster of professions and the repeated "expert" framing could be cut without loss.

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 one-optional-param, no-output-schema read tool, the description is substantially complete: it spells out the categories of data returned and the optional field filter. Missing is how the returned data relates to sibling tools that also report pay and market data, which is the one real 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?

Schema coverage is 100% and the single enum parameter is fully documented with examples in the schema, so the baseline of 3 applies. The description's only contribution is "Optionally one field," which restates the already-documented optionality without adding filtering semantics or scope implications.

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 uses a concrete verb family ("Returns what it delivers, the network's live counts by field, what each kind of expert is paid..., how projects are priced") and names the vendor, so an agent can tell this is a vendor-profile/data-lookup tool. It does not differentiate itself from siblings, and it overlaps visibly with get_ai_training_pay by claiming to return pay figures without explaining the split.

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 only usage signal is "For teams building AI who need expert human data" — that describes an audience, not a tool-selection condition. No when-to-use, when-not-to-use, or alternative is named, even though get_ai_training_pay and get_ai_training_market sit right beside it and plausibly cover the same questions.

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, non-destructive and openWorld, so the safety profile is covered. The description does add useful scope detail that annotations cannot: the dataset spans multiple named platforms and includes pay, field, countries, open/close dates, per-platform daily rows and a change log. It omits any indication of response size, freshness/cadence, or whether the price/sample info constitutes an external action.

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?

It is a single long run-on sentence that mixes a product pitch, a data dictionary and a return-value list. The most decision-relevant part (what it returns: files, users, price, sample) is buried at the end rather than front-loaded, and phrases like 'since its first read' add cost without 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 no output schema, the description carries the burden of explaining returns and it does so adequately: coverage, files, users, price, sample-access path. Given the simple zero-param shape and existing annotations, this is close to complete; the missing piece is how it relates to the overlapping catalog siblings.

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 schema imposes nothing and the baseline is 4. Nothing in the description misleads about inputs; the emphasis on 'returns' correctly signals a no-arg lookup.

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 (AITraining.jobs' hiring-radar dataset) and, crucially, what the tool returns: coverage, files, users, price, sample access. An agent can tell this is a dataset-catalog lookup rather than a data query. It never distinguishes itself from get_ai_training_market or get_ai_training_pay, which read like overlapping dataset-info 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?

There is no when-to-use guidance at all. With four siblings covering AI-training market, pay, expert services and job search, the description gives no signal about when an agent should call this catalog tool instead of those. The reader must infer intent from the marketing copy.

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 readOnlyHint, idempotentHint and openWorldHint already declared, safety is covered. The description earns credit by enumerating the return payload — title, platform, kind of work, advertised pay, location openness, apply link — which matters because no output schema exists, and by flagging that the apply link points to AITraining.jobs rather than a direct employer site.

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 with the core action front-loaded and the return shape second; nothing is wasted. The platform roster is slightly listy but serves to establish coverage breadth, which an agent needs to judge relevance.

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 read-only search with no output schema, the description supplies the returned fields and the domain, which is the important missing piece. Minor gaps remain: result ordering/sort behavior, how query keywords are matched, and whether 'country' means eligibility or location are not stated, but none blocks correct invocation.

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 (kind enum, limit bounds, country ISO code, platform slug, min_hourly_usd semantics) is already fully documented. The description adds only the '63 platforms' scale figure and the reminder that filters are optional, which is marginal value over 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 ('Search the open AI training jobs'), defines the domain precisely as 'paid work training and evaluating AI models', and names the source platforms. An agent knows this is a job search over a curated marketplace, though the description never names the sibling tools it differs from (e.g., get_ai_training_market), so the differentiation is implicit.

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?

'All filters are optional' usefully signals that the tool can be called with no arguments and that filters narrow rather than require. However, there is no statement of when to use this instead of get_ai_training_market, get_ai_training_pay, or get_hiring_radar, so the agent must infer routing from sibling names alone.

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