Free AI For That

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, so the safety profile is covered. The description adds real value beyond that by disclosing the returned contents, including freshness ('when it was last checked') and provenance ('link... which has the dated source'), which an agent cannot infer from annotations alone.

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?

The core purpose and accepted inputs are front-loaded in the first two sentences, with fallback behavior and provenance following in a logical order. It is somewhat dense in enumerating return contents, but every clause carries information an agent would otherwise lack.

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, yet the description covers the return shape, fallback behavior for non-free tools, freshness, and where the full detail lives. For a single-parameter read-only lookup, nothing essential is missing.

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 single 'tool' parameter is already documented. The description goes further with concrete accepted formats ('ChatGPT', 'Midjourney', 'canva.com'), making clear both display names and domains work, which is more than the schema's bare 'name or website' wording.

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: 'Check whether a specific AI tool is free and exactly what its free plan includes.' It enumerates the answer surface (limits, catches, cheapest paid plan, last-checked date), which clearly separates it from find_free_ai_tools (browse) and compare_free_plans (multi-tool comparison).

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 implies the right context by describing a single-tool lookup with a name or website input, and notes the fallback behavior when a tool isn't free ('says why and suggests free alternatives'). However it never explicitly states when to reach for this instead of the sibling compare_free_plans or find_free_alternatives, leaving routing to inference.

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, and closed-world behavior. What the description adds is valuable: the content of the result (what's free, limits, catches, how to start, checked dates) and the presence of a side-by-side table link, which matters since there is no output schema. It stops short of stating behavior on invalid tool names or how results are ordered.

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 tightly packed sentences, front-loaded with the comparison payload before the secondary link explanation. Slight redundancy in describing both the side-by-side table link and the per-tool page link, but nothing is wasted.

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 return-value burden and does so reasonably well by enumerating the fields returned. Remaining gaps are error behavior and result structure, minor for a one-parameter read-only 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?

Schema coverage is 100% and the single parameter is already described as 'Tool names or websites to compare.' The description restates the 2-to-4 cardinality but adds no new meaning about accepted name formats, ambiguity handling, or matching behavior, 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 (compare free plans) plus a scope constraint (2 to 4 AI tools side by side), which implicitly separates it from the single-tool sibling check_free_plan. It never names that sibling, so the differentiation is inferential rather than explicit.

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 the '2 to 4 tools side by side' phrasing, which suggests the multi-tool case, but there is no explicit when-to-use statement, no exclusion pointing to check_free_plan for a single tool, and no prerequisite guidance.

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 read-only, idempotent, non-destructive, and closed-world, so the safety profile is settled. The description adds genuinely new behavior: results are ranked by how generous the free option is, and each result carries what's free, limits, the catch, how to start, and a last-checked date — details an agent cannot get from annotations.

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 the purpose before the return-value detail, with no filler. The return-contents sentence is dense but every clause describes a distinct attribute an agent would act on.

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 return values and does so thoroughly, including the ranking criterion and the per-tool fields. A ranked lookup with a fully documented schema and safe annotations needs little else; only sibling routing is left unaddressed.

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 query, limit, and no_signup are already documented in structured data. The description adds only illustrative query phrasings and says nothing about limit or no_signup that would extend the schema, so the baseline of 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 concrete verb and resource ('Find free AI tools for a task or need') and anchors it with three realistic query examples, so the agent knows exactly what it searches over. It does not explicitly differentiate itself from the sibling find_free_alternatives, which overlaps on the 'free alternative to Canva' case.

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 example queries imply the right kind of input (a job, need, or named tool) and thereby hint at when this tool applies. However, there is no explicit when-not-to-use guidance and no routing to check_free_plan, compare_free_plans, or find_free_alternatives, leaving sibling selection to inference.

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 read-only, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavioral detail: results are ranked by free value, each carries free-plan specifics and a checked date, and each links to a dated source page. That output-shape disclosure is valuable given no output schema exists.

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 the core action and examples. The second sentence partly restates the free-plan/date content of the first, which is minor redundancy but keeps the return-shape information tight.

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 simple two-parameter read tool with full schema coverage, the definition covers intent, ranking, and return contents without an output schema to lean on. Only the sibling disambiguation and the limit behavior are left implicit.

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%, including a description for 'tool' and bounds/defaults for 'limit', so the baseline is 3. The description's example tool names add mild disambiguation for the 'tool' parameter, but the 'limit' parameter is never addressed.

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 free alternatives') plus concrete example inputs ('Midjourney', 'Grammarly'), so the agent knows exactly what comes back. It does not explicitly distinguish itself from the sibling find_free_ai_tools, leaving the boundary between 'alternatives to X' and 'find tools' to be inferred.

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 'to replace' a paid or popular tool, which signals the intent reasonably well. However, no sibling is named as an alternative, and there is no explicit when-not guidance (e.g. use check_free_plan when you already know the tool).

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