EveryAI

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds value by disclosing what the response conveys beyond a boolean: the listing status plus the owner's next possible action (claim vs. list), which is the key behavioral trait here.

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, front-loaded with the core answer ('whether a website already has a live EveryAI listing'), with the follow-up outcome in a parenthetical. Slightly convoluted phrasing but no wasted content.

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 must convey what comes back, and it does at a high level (status plus next-action guidance). It stops short of describing the response shape or how 'not listed' is represented, but for a one-parameter read-only check this is close to sufficient.

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?

There is a single required parameter with 100% schema description coverage and an inline example, so the schema carries the semantics. The description adds nothing about URL form (http vs. https, trailing slash, whether a bare domain is accepted), so it stays at the baseline.

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 names the specific resource (a live EveryAI listing for a website) and the specific question it answers (listed or not). It is distinguishable from siblings like get_tool or search_tools because it is about listing status rather than tool content, though it never explicitly contrasts itself with 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 'what its owner can do next (claim it, or list it if it is not there yet)' implies this is a pre-submission/pre-claim check, but it never states when to call it versus get_tool, search_tools, or prepare_submission. Usage is inferable but not explicit, and no exclusions are given.

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 profile is covered. The description adds real business context not in annotations: the price is one-time with no subscription, and the response covers what's included plus purchase steps. It doesn't say whether pricing is guaranteed/current or what happens for unsupported countries.

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 filler. The what-is-returned content leads and the parameter instruction follows, which is the right ordering for an agent deciding whether to call it.

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-parameter read-only lookup with a fully documented schema, the description covers purpose, price model, and the country-to-price relationship. It's slightly thin on what the returned offer actually looks like (fields, whether a purchase link is included), but that's a minor gap given the simple surface.

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 already documents the ISO 3166 alpha-2 format with examples, so the baseline is 3. The description adds the meaningful detail that country drives a localized price — explaining why the parameter exists, not just its format.

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 names the specific resource (Featured listing pricing on EveryAI) and enumerates its payload: cost, inclusions, and purchase process. That's a clear verb+resource picture. It doesn't explicitly name a sibling like prepare_submission to distinguish itself, keeping it at 4.

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?

'How a founder buys it' and 'Pass the founder's country for their price' imply the usage scenario (a founder evaluating/acquiring a Featured listing), but there is no explicit when-to-use versus when-to-use-something-else guidance. With a sibling like prepare_submission clearly adjacent, an explicit boundary would have earned a 4 or 5.

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, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered by structured data. The description adds only that the returned data is 'full public details', which is modest additional context; no rate limits, auth needs, or failure behavior (e.g. not-found) are mentioned.

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 names the resource, the payload, and the lookup keys with zero 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?

For a one-parameter read tool with full annotation coverage, the description plus schema gives an agent everything needed to invoke it correctly, and it even previews the return payload despite there being no output schema. Minor gap: no note on behavior when the identifier matches nothing.

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% and the schema example already lists all four accepted identifier formats, so the description's enumeration of URL/slug/website/exact name largely restates structured data. Baseline 3 is appropriate since the schema does the heavy lifting.

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+resource (retrieve full public details of one listed tool) and enumerates the returned fields (description, features, category, links). The phrase 'one listed tool' implicitly separates it from search_tools, but no sibling is named explicitly.

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 four accepted identifier forms imply the usage condition (call it when you already have a URL, slug, website, or exact name), but the description never states when to prefer search_tools instead when you only have partial or fuzzy keywords.

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, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered. The description adds content-level context (results include tool counts and hub pages) but says nothing about ordering, pagination, or result shape.

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 short clause with no filler, front-loading the resource name and the most useful payload detail (tool counts). It is a sentence fragment rather than a complete sentence, which is a minor style cost but not a clarity problem.

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 must carry the return-value burden, and it does name the key fields returned (categories, subcategories, hub pages, tool counts). It does not describe nesting or ordering, but for a zero-parameter list tool this is close to sufficient.

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 there is nothing to document and the baseline of 4 applies. The description correctly implies an unfiltered full listing.

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 names the resource (EveryAI categories) and enumerates what each entry contains: use cases/subcategories, hub pages, and tool counts. This distinguishes it from siblings like search_tools or get_tool, though it omits an explicit verb such as 'list'.

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 statement of when to call this versus search_tools or get_tool, and no prerequisites or exclusions. The agent must infer from the name alone that this is a discovery/browse entry point.

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 destructiveHint=false, and the description reinforces this with the crucial fact that nothing is submitted and the founder performs the actual submission and payment. This is the key behavioral distinction an agent needs and it would not be inferable from the annotations alone. It omits secondary traits such as link expiry or reuse, which 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?

Three sentences, each doing distinct work: what is returned, what is not done and who does it, and the already-listed fallback. The scope statement is front-loaded and there is no 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 no output schema and no annotations covering returns, the description correctly carries the burden by describing both possible outcomes: a prefilled link, or the existing listing plus claim instructions. It does not say anything about the link's lifetime or whether prefilled fields can be edited before submission, which are the remaining unknowns for a 7-parameter 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 71%, so most parameters already carry their own documentation and the baseline is 3. The description only says the form is 'prefilled with the details you pass' without adding per-parameter meaning, and it does not compensate for the two undocumented fields (name, category). Adequate, but not additive.

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: 'returns a link to the submit form prefilled with the details you pass', plus the fallback case where the site is already listed. That is specific enough to know exactly what comes back. It does not name the closely related sibling check_listing, which appears to overlap with the 'already listed' branch, so sibling differentiation is only implied.

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?

Opens with the triggering situation ('For a founder who wants their tool on EveryAI'), which gives the agent a clear context for use. It also explains who completes the final action, so the agent knows this tool is a preparation step, not the submission itself. It stops short of explicitly routing the agent to check_listing or search_tools instead when they only want to verify an existing listing.

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 this is a safe, idempotent read with no external side effects, so the description focuses on what annotations cannot express: the response fields (page, website, category, description) and the ranking bias disclosure that "featured" listings are paid placements. That paid-placement caveat is genuinely useful for an agent weighing results, though ranking logic beyond that is unstated.

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 front-loaded sentences covering purpose, examples, return shape, and the featured caveat, with no filler. The paid-placement note could arguably be folded into the return sentence, but its separation makes it stand out.

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 correctly summarizes return fields and adds the sponsored-result caveat, which is what an agent most needs to interpret results. Minor gaps remain around pagination and how limit interacts with ranking, but nothing essential to calling the tool 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 67% and the schema itself explains both q (empty lists newest, featured first) and category. The description adds only phrasing guidance for q via example queries and says nothing about the limit parameter's role. Baseline 3 is appropriate given the schema does most of the work.

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 EveryAI directory for AI tools") and reinforces it with two concrete example queries. It does not explicitly distinguish itself from siblings like get_tool or list_categories, leaving that differentiation to the names alone.

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?

Gives a clear usage context — describe the task in natural language rather than an exact tool name — and the schema covers the empty-query fallback for browsing newest tools. It offers no explicit exclusions or pointers to alternatives such as get_tool for a known tool, so an agent must infer the boundary.

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