Automate & Tweak AI Tools Directory

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds genuine value beyond them by disclosing that the comparison-page URL is conditional ('when that pair has a public comparison page') and that the tool never creates comparisons, i.e. it only surfaces existing site data. It does not say what the response contains when no public page exists, which keeps it from a 5.

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 with zero filler: the first front-loads what is returned, the second qualifies the one conditional behavior. Every clause carries information an agent cannot get from the schema alone.

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 enumerates the returned fields and the optional comparison URL, so an agent can predict the response shape. Gaps are minor: the effect of the locale parameter on returned names and URLs is left to the schema, and error behavior for unknown slugs is unstated.

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 both parameters are documented with enum values and a default, so the schema does the heavy lifting. The description reinforces the slugs cardinality ('2 or 3 tools') consistent with minItems/maxItems, but adds nothing about the locale parameter or slug format beyond 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?

The description names a specific resource (side-by-side data for 2 or 3 tools) and enumerates the returned fields (pricing, categories, pros and cons, rating, website), which implicitly separates it from the single-tool sibling get_tool. It stops short of explicitly naming an alternative, so it is clear but not maximally differentiating.

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 context is only implied: the '2 or 3 tools' framing tells the agent to pick this over a single-tool call, and 'this tool never creates comparisons' rules out a write/creation expectation. There is no explicit when-to-use statement, no mention of the locale parameter's purpose, and no guidance on what to do when a requested tool slug does not exist.

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 behavioral context beyond that: the exact payload contents and, notably, that offline or merged tools report their state and return a replacement or alternatives rather than failing.

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 identity of the resource and followed by the payload summary. The long field enumeration is dense but each item earns its place since there is no output schema to carry that 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 carries the burden of describing return values and does so thoroughly, plus it covers the important edge case of offline/merged tools. The only gap is the absence of routing guidance to sibling tools, which matters less for a lookup-by-key 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% — slug and locale are both documented in-schema, including the example slug and the locale enum with its default. The description only echoes "by slug" and adds no format, casing, or locale behavior beyond what the schema already says, 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?

The description states a specific verb and resource ("Full public details of one AI tool by slug") and enumerates the returned fields, which clearly separates it from list-style siblings like list_tools and search_tools. It does not name any sibling explicitly, so the differentiation is implicit 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: an agent infers it should call this when it already has a slug and needs the full record. There is no explicit when-to-use versus search_tools or list_tools guidance, and no stated prerequisites, though the slug-based framing is a reasonable signal.

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 the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description just needs to add context. It does so by disclosing that only 'active' categories are returned and what fields come back (localized names, live tool counts, canonical URLs). It does not describe ordering or pagination behavior, which keeps it below a 5.

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 short sentences with no filler; the payload summary comes first and the actionable slug guidance second. 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?

For a single-optional-parameter, read-only listing tool with no output schema, the description covers what is returned and how to reuse the result. The only omission is the response shape or ordering, which is minor for a simple category list.

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 an enumerated `locale` and a clear description of its effect, so the schema already carries the parameter semantics. The description's 'localized names' adds only a light hint that locale affects output, rather than new syntax or defaulting guidance. Baseline 3 is appropriate when 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?

The description names the exact resource (tool categories) and the payload it returns: localized names, live-tool counts, and canonical URLs. It is clearly distinguishable from siblings like list_tags, list_types, and list_tools because it explicitly says 'tool categories'. It falls short of a 5 only because the verb is implicit rather than stated as 'List/retrieve'.

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 second sentence gives a concrete downstream use: take the slug and pass it as the `category` filter to search_tools or list_tools, which implicitly explains why an agent would call this tool first. There is no explicit exclusion or 'do not use when' guidance, so it stops short of a 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, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description adds genuine behavioral detail beyond that: only *active* pricing types are returned, and the payload contains localized names plus a live-tool count per entry.

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 short sentences, front-loaded with the resource and its payload, followed by the one actionable instruction. Zero filler or restatement of 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 no output schema, the description responsibly describes the return fields and their localization. For a one-optional-parameter lookup tool that is close to sufficient; the only real gap is the lack of differentiation from list_types.

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 locale enum is fully documented with a default, so the baseline is 3. The description's mention of 'localized names' reinforces that locale changes output language, but adds no syntax or format information beyond 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?

The description states a specific resource ('active pricing types', with examples free/freemium/subscription) and enumerates what is returned (localized names, live tool counts, canonical URLs). It does not, however, distinguish itself from the sibling list_types, which an agent could easily confuse with 'pricing types'.

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 second sentence gives downstream guidance ('Use the slug as `pricing` filter'), which tells the agent what to do with the result but not when to choose this tool over siblings like list_types, list_categories, or list_tags. Usage is implied rather than contrasted.

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 that this is a safe, read-only, idempotent, non-destructive, non-open-world operation. The description adds substantial behavioral context beyond annotations: only active tags are returned, names are localized, each tag includes live tool counts and canonical URLs, and results are sorted by usage.

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 description is two compact sentences with no filler. The key return characteristics are front-loaded, and the final sentence gives actionable downstream guidance about the slug.

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 read-only list tool with one optional parameter, no output schema, and rich annotations, the description covers the essential behavior: active-tag scope, localized fields, live tool counts, canonical URLs, ordering, and slug reuse. Nothing critical for correct invocation appears to be 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%, and the `locale` parameter is already fully documented in the schema as controlling language for names, descriptions, and URLs. The description's reference to localized names and canonical URLs aligns with that schema but does not add new parameter-level syntax or constraints.

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 verb and resource: it lists all active tags. It also clarifies scope and returned content by mentioning localized names, live tool counts, canonical URLs, and most-used-first ordering. This is clearly distinguishable from sibling list tools such as list_categories and list_pricing_types.

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 implicitly clear because the tool is specifically for retrieving tags, and the description adds that the returned slug should be used as a `tag` filter elsewhere. However, it does not explicitly compare this tool with alternatives such as search_tools or list_categories, nor does it state when not to use it.

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?

The annotations already establish safe, read-only, idempotent behavior, and the description adds site-like ordering, a newest-first default, and cursor-based pagination. Telling the agent to pass the returned `next_cursor` as `cursor` is actionable, although much of this operational detail is also present in the schema.

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 front-load the non-search browsing use case, then cover ordering and pagination without filler. The wording is dense but clear, and each clause supports selection or invocation.

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?

Together with the rich schema and annotations, the description is sufficient to make a correct first call and retrieve subsequent pages despite the absence of an output schema. Explicit sibling routing and a brief indication of what each result contains would improve completeness, particularly because only `next_cursor` is named from the response.

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%, already documenting every filter, enum, default, limit, locale, and cursor. The description usefully frames the filters as optional and connects the pagination workflow, but adds little parameter syntax or semantics beyond the schema, so the high-coverage baseline 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 title supplies the AI-tool resource, while the description specifies browsing the directory with optional category, tag, listing-type, and pricing filters. “Without a search text” meaningfully distinguishes this browsing operation from search_tools rather than merely restating the tool name.

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 clearest selection cue is to use it for browsing without search text, optionally applying faceted filters. It does not explicitly route keyword queries to search_tools or clarify when new_tools is preferable, so the guidance is useful but not exhaustive.

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, closed-world, so the safety profile is covered. The description adds genuine context beyond them: only *active* types are returned, and each entry carries localized names, a count of live tools, and canonical URLs. It does not mention ordering or whether counts respect any locale/filter scope, which keeps it short of a 5.

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 resource and its contents, followed by the one actionable hint. Every clause earns its place and nothing is repeated from the schema.

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 describing return values and does so adequately (localized names, live tool counts, canonical URLs, slug as identifier). For a zero-required-parameter enum lookup tool this is nearly complete; ordering and whether inactive types are ever relevant are the only minor omissions.

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 single `locale` parameter has a full enum with a clear description of what it localizes. The tool description adds nothing about `locale` beyond what the schema already says, so the baseline 3 is appropriate.

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 the specific resource and its scope: 'All active listing types' with examples of what a type is (online tool, app, plugin), so an agent can distinguish it from siblings like list_categories or list_tags. It also enumerates the fields returned. Lacks an explicit verb but the listing semantic is unambiguous.

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 second sentence gives a downstream-use hint ('Use the slug as `type` filter'), which implicitly explains why an agent would call this — to discover valid type filter values. However, it never says when to choose this over list_categories/list_pricing_types/list_tags, nor does it name which tool the slug feeds into. Usage is implied rather than stated.

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, openWorldHint=false and destructiveHint=false, so the safety profile is fully covered. The description adds the sort order (newest first) and the fields returned, which is genuine extra context, but says nothing about pagination, the 25-item cap, or locale effects on results.

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 identifies the resource, the ordering, and the returned fields with zero filler. Nothing could be removed without losing 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 simple, zero-required-parameter read tool with rich annotations and a fully documented schema, the description covers purpose, ordering, and return content. The only gaps are pagination/limit-cap behavior and how locale affects output, both minor and partly inferable from the schema.

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%: limit (1-25, default 10) and the locale enum with its default are fully documented in the schema. The description adds no additional meaning about how locale changes names/descriptions/URLs or how limit interacts with the recency ordering, 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 resource (AI tools) plus the defining scope ('most recently listed, newest first') and names the payload fields (listing dates, canonical URLs). An agent can distinguish it from list_tools by the recency focus, though it never names the sibling 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 'most recently listed' framing implies a discovery/browse use case, but there is no explicit when-to-use statement, no exclusion, and no pointer to list_tools or search_tools for the full-catalog or filtered cases.

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?

The annotations already declare readOnlyHint, idempotentHint, and non-destructive, so the safety profile is covered; the description adds real value on top by disclosing relevance-based ranking and the return shape ("compact tool cards with canonical URLs"). It does not say what happens with zero matches or whether results are ever truncated, which keeps it below a 5.

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 dense sentences: search scope first, filter options second, return shape and next-step routing last. Nothing is duplicated and no sentence could be removed without losing information the agent needs.

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 seven-parameter search tool with no output schema, the description supplies the indexed fields, ranking behavior, filter dimensions, and return-card shape, which is nearly everything needed to call it correctly. The remaining gap is edge-case behavior (empty result sets, result caps beyond the schema's limit) which the agent must infer from the schema.

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 every parameter (including the pricing synonym rules and the list_* cross-references) is already documented in the schema. The description only restates the filter categories at a high level, adding no syntax, constraint, or interaction detail beyond the structured fields, 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 verb and resource ("free-text search over the AI tools directory") and enumerates the indexed fields (names, aliases, subtitles, descriptions) plus the optional filter dimensions, so the agent knows exactly what kind of operation this is. It separates itself from get_tool but never contrasts with the sibling list_tools, which is the closest ambiguous alternative for an agent with no query in mind.

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?

Clear routing context is given: use this for free-text lookup, then "call get_tool for details" once a candidate is found, and the optional filters are framed as refinements. There are no explicit exclusions (e.g., when to prefer list_tools or list_categories instead), so it stops short of full when/when-not guidance.

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