AI Trainer Jobs

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and openWorldHint=false, so the safety profile is covered. The description adds useful scope about what the profile contains, but says nothing about error behavior for unrecognized company names, rate limits, or freshness of the data.

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 efficient sentence that front-loads the verb and resource, then lists content dimensions. The parenthetical 'good and bad' and the quoted example question are slightly ornamental but still clarify intent.

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 enumerate return fields, yet it usefully summarizes the profile's scope so an agent can judge relevance. The only real gap is the absence of routing guidance against sibling tools.

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?

Only one parameter, and schema coverage is 100% with the schema enumerating all 25 valid company names and length bounds. The description adds nothing about the name argument, so the schema does all the work; 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?

States a specific verb and resource ('Get one AI training company's profile') and enumerates the content dimensions the profile covers (what it does, pay, hiring, worker sentiment, FAQs). It does not explicitly differentiate itself from siblings like list_companies or get_job, though 'one ... company' implies single-entity lookup.

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: the required 'name' parameter plus 'one company' suggests this is the per-company lookup path, while list_companies would be the browse path. No explicit when-to-use, when-not-to-use, or alternative is named.

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, destructiveHint=false and openWorldHint=false, so the safety and idempotency profile is fully covered without the description. The description adds only a listing of returned fields, which is largely duplicative of the output schema, and says nothing about error behavior for a malformed or unknown id.

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, no filler, with the core action and identifier front-loaded and the field enumeration following. 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?

With annotations covering safety and an output schema covering return structure, the description supplies what the agent needs to select and call the tool. It is slightly over-complete in re-listing return fields already in the output schema, and silent on failure behavior for an invalid id.

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?

A single parameter with 100% schema description coverage (the schema itself explains the 8-hex-character format and provenance). The description restates the id source but adds no syntax or format detail beyond what the schema already documents, 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?

States a specific verb and resource ('Get one AI trainer job by its id') and enumerates exactly what the returned job record contains (pay, location, languages, level, hours, requirements, company description). Naming search_jobs as the id source cleanly separates it from the sibling list tool.

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 parenthetical '(from search_jobs)' establishes the intended workflow context — this is the detail-fetch step after a search. There is no explicit when-not guidance or mention of how it relates to get_company, but the usage context is clear.

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 closed-world, so the safety profile is fully covered. The description adds useful scope context (only companies with open jobs) but no further behavioral detail such as ordering or result size.

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 sentence front-loads the resource and field list, followed by a short directive. Slightly compressed and somewhat enumerative, but there is no wasted 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?

With zero parameters, full annotation coverage, and an output schema present, the description needn't explain return values, and it still sketches them. It is adequately complete for such a simple read tool, though the comparison guidance could be sharper.

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; there is nothing for the description to disambiguate.

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 (List) and resource (companies with open AI trainer jobs) plus the scope of the dataset (aitrainerjobs.co). The singular/plural contrast with siblings get_company and get_job makes the distinction immediately clear.

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 says 'Use it to compare companies,' giving a clear use case that routes the agent away from get_company (single-lookup). It stops short of stating when-not to use it or naming the sibling 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 establish the safe-read profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint=false), so the bar is low. The description adds genuinely non-structured behavior: result ordering (newest first), which matters for reasoning about offset-based paging, plus the field set returned. It does not cover rate limits or result-count caps beyond the schema's limit.

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, front-loaded with purpose and back-ended with ordering/returns; nothing is wasted except the trailing 'All filters are optional', which duplicates the schema's zero required parameters. Slightly tighter would be 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?

For a 7-parameter, all-optional search with a full schema description set, an output schema, and safety annotations, the description supplies the domain framing and sort order an agent needs to decide to call it. It stops short of explicit routing advice to the sibling lookup tools, which is the only meaningful 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 description coverage is 100%, so every parameter (limit, query, offset, category, pay_type, min_hourly_pay, eligible_country) is already documented with examples and enum semantics. The description only adds the redundant 'all filters are optional' note, 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?

States a specific verb and resource (search open AI trainer jobs) and immediately scopes the domain with an explicit enumeration of job areas. It is trivially distinguishable from the retrieval siblings get_job, get_company, and list_companies, which fetch single entities or companies rather than searching a job corpus.

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' and 'Returns the newest jobs first' imply that this is an unfiltered browse/search entry point, but there is no explicit when-to-use vs. when-not guidance and no mention of the alternatives (e.g., use get_job once you have an id). Usage is inferable rather than stated.

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