Compare AI Workers

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

No annotations exist, so the description carries the full burden. It usefully discloses that pricing is an approximation based on the monthly low-end estimate and lists returned fields, but does not state that the operation is read-only, describe result ordering, or mention any rate/latency or auth considerations.

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; the core purpose and scope lead, and the optional filter behavior follows. No redundant or filler text.

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 two-parameter read tool with no output schema, the description is nearly sufficient: it names the returned fields and explains the filter's semantics. The only gap is lack of explicit read-only/behavioral framing, which matters given the absence of annotations.

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 documented. The description adds only that max_budget_usd filters to vendors whose approximated monthly low-end price fits, which largely restates the schema's 'low-end estimate filter' note, and it does not add the enum-style category values already present in 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 ('Compare AI worker vendors'), scopes it to one category, and enumerates what is returned (names, pricing hints, positioning, URLs). This clearly distinguishes it from the single-entity sibling get_vendor and the enumeration sibling list_categories.

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 usage through 'in one category' and explains the optional budget filter's effect, but never states when to reach for this over get_vendor or list_categories, nor any exclusions or prerequisites. Usage is inferable 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?

No annotations are provided, so the description carries the behavioral burden. 'Get detail' implies a read-only lookup and the listed return fields add useful context, but it does not cover not-found behavior, permissions, rate limits, or data freshness.

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 followed by a compact colon-separated list of returned fields. No wasted clauses.

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 lookup with no output schema, the description names the returned fields and gives examples, which is enough for an agent to call it correctly. It is not fully complete because it omits error/not-found behavior and any indication of data freshness.

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 the baseline is 3. The description adds concrete example vendor names ('11x', 'Smith.ai', 'Intercom Fin') that clarify the expected input format beyond the schema's case-insensitive note.

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 detail') and resource ('AI worker vendor by name') and enumerates returned fields. It does not explicitly name the sibling alternatives, though the single-vendor lookup is distinguishable from compare_ai_workers and list_categories.

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 implied by 'by name' and the examples, but there is no explicit when-to-use, when-not-to-use, or routing guidance to compare_ai_workers or list_categories.

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 exist, so the description carries the full burden of behavioral disclosure. It does disclose the return content (vendor counts per category), which is genuinely useful, but says nothing about whether this is a read-only call, ordering, or how the counts are scoped.

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 parenthetical category enumeration is informative rather than padding, though it is the densest part of the sentence and could be trimmed if the list is volatile.

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 zero-parameter read tool with no annotations and no output schema, the description covers what it does and what it returns (categories plus vendor counts). A brief note on its relationship to compare_ai_workers is the only material 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 and there is nothing for the description to compensate for. No parameter claims are made or 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?

States a specific verb and resource ("List the AI worker categories") and enumerates the categories returned, so the agent knows exactly what comes back. It does not differentiate itself from the sibling compare_ai_workers beyond the passing mention that these are the categories "compared by" that 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?

No indication of when to call this versus compare_ai_workers or get_vendor, despite both being plausible entry points. The agent is left to infer that this is a cheap discovery/metadata call and that comparison work belongs to the sibling.

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