aitrainers.jobs: AI training jobs search

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety and scope are covered. The description adds that only 'open' jobs are counted and that results are grouped by work kind and platform, which is useful, but it says nothing about how counts are returned or whether the grouping is comprehensive.

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 states the count, the scope of jobs, both grouping dimensions, and the optional filter, with no filler. Every clause carries 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?

With no output schema, the description does a fair job of implying the return shape through 'by kind of work and by platform', and it clarifies the 'open' scope. What's missing is a statement of how the grouped counts are returned or whether zero-count buckets appear, which an agent would want for a counting 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?

The single parameter is fully documented in the schema, including the two-letter ISO pattern and the important edge case about roles with no stated location being included after country-specific ones. The description only echoes the optional country filter ('optionally for one country') and adds no syntax detail beyond the schema, so the 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 states a specific resource (open AI training jobs) and a specific output shape (counts broken down by kind of work and platform), which is much more informative than the title 'Count AI training jobs'. It doesn't explicitly contrast with the search_jobs sibling, so differentiation is inferred rather than stated.

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: 'how many' suggests aggregate counting rather than listing or filtering, which is the natural complement to search_jobs. There is no explicit 'use this when you want totals instead of individual postings' statement and no guidance on when the country filter is worthwhile.

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=true and openWorldHint=false, so safety is covered; the description adds the valuable non-obvious detail that returns are per-role with pay, hiring location and a link to the listing page. It doesn't state rate limits or result-set caps, but for a read-only search over a fixed corpus that is a minor 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, zero waste: the first front-loads what is searched and the filter axes, the second states the shape of the return. Nothing redundant with the 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?

With 10 optional parameters fully documented in the schema and no output schema, the description's coverage of what a result record contains closes the main gap. Pagination and ordering are covered by schema descriptions rather than prose, which is acceptable given zero required parameters.

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% with per-field descriptions and enum mappings, so the schema carries the parameter semantics. The description only restates the filter dimensions (country, kind of work, language, minimum pay, experience) and omits q, sort, limit and offset, adding no meaning beyond the structured fields; 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?

Specific verb and resource ('Search open paid AI training jobs ... listed on aitrainers.jobs') plus an enumerated filter set and a statement of what is returned. It is clear what the tool does, but it never distinguishes itself from the sibling job_counts, so the agent must infer the split between listing roles and counting them.

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 'open paid AI training jobs' and the filter list imply the search use case, and the note that results include pay, location and a link clarifies the intended output. However there is no explicit 'use this when / use job_counts instead when' guidance and no exclusions, so the sibling choice is left to inference.

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