KI Alternativen

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

Annotations already indicate this is not read-only, but the description adds crucial behavioral context: nothing is charged until a human completes checkout in a browser, and it should never be presented as a completed purchase. This goes beyond what annotations provide. It also discloses error scenarios. There is no contradiction with annotations, so this is a solid score.

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 three sentences, front-loaded with the primary action, followed by the critical caveat and error conditions. Every sentence earns its place, with no fluff or repetition. It is concise yet comprehensive.

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?

Given the tool's low complexity (2 simple params, output schema exists), the description is complete. It covers the action, the crucial non-completion caveat, and all error conditions. The output schema handles return values, so no further explanation is needed. This is a well-rounded, self-sufficient description.

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 input schema already provides 100% coverage with descriptions for both 'slug' and 'product', including an enum with examples. The description adds little beyond the schema, merely implying that the add-on must be 'paid' and mentioning a 'free early access' error condition, which is already implicit in the enum. Baseline 3 is appropriate as the schema handles the parameter semantics.

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 clearly states the action: 'Create a Stripe checkout URL for one paid add-on on one listing'. It specifies the resource (checkout URL) and scope (one add-on, one listing), and distinguishes it from sibling tools like list_offers by focusing on creation rather than listing. The additional note that it is for a human to open further clarifies its unique role.

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 description provides clear context for when to use the tool—when creating a checkout link for a paid add-on. It includes important usage guidance like 'Never present this as a completed purchase' and lists error conditions. However, it does not explicitly mention alternatives or when not to use it, such as referencing list_offers for checking offer details, which would be a natural next step.

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 and idempotentHint=true, so the description doesn't need to restate safety. Instead, it adds behavioral context: returns null if slug doesn't resolve, indicates data fields returned (full description, launch date, revenue signals, for-sale status). Slightly unclear whether 'verified or self-reported' means revenue can be of different types, which could confuse but doesn't harm. Minor deduction for not saying anything about auth or rate limits, but those are likely covered by sibling docs.

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 primary action, and includes a concrete example. No wasted words, every clause serves a purpose.

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?

The tool is simple (1 param), has a rich schema, and an output schema exists. The description covers the main return fields, null handling, and relationship to sibling tools. A more explicit note on response example could push it to 5, but it's already quite complete for its complexity.

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% ('The tool's listing slug.'), so the parameter semantics are fully documented in the schema. The description adds an example slug format and ties it to the search_tools toolUrl, which is extra context. Per rubric, baseline of 3 applies when coverage is high, and this matches.

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 clearly states the tool 'Fetch the full public detail for one AI tool by its listing slug' – a specific verb+resource combination. It distinguishes this tool from siblings by explicitly referencing search_tools' output and by naming the exact use-case for retrieval.

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 provides explicit usage guidance: 'Call this after search_tools' and describes what data to retrieve. It also clarifies the null return case for a non-existent slug, covering an edge case that enriches the usage context.

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 readOnly and idempotent behavior, so the description does not need to restate safety. It adds useful behavioral context by specifying the results are 'live', the alternatives are 'same-category', and `target` must not be a catalog listing slug.

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 compact: one strong main sentence, one trigger-and-disambiguation sentence, and negligible filler. It is front-loaded with the core action and reads naturally for an AI agent.

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, low-complexity, read-only tool with a supporting output schema, the description fully covers the intended use, target semantics, and disambiguation from search_tools. There are no major omitted details.

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?

Although the schema already fully documents the single parameter, the description adds important disambiguation: `target` must be the well-known tool's slug and explicitly not a listing slug from the catalog. Examples like 'chatgpt' and 'notion-ai' reinforce this.

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 and resource: 'List live, same-category alternatives to a well-known AI tool.' It gives concrete examples ('chatgpt', 'notion-ai', 'figma') and clearly distinguishes itself from catalog search tools by requiring a well-known tool slug rather than a listing slug.

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 explicitly says, 'Call this when a user asks...', which gives clear trigger conditions for use. It does not explicitly name sibling tools as alternatives, but the target-slug caveat helps prevent a common misuse.

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 indicate a safe read-only, idempotent operation. The description adds valuable behavioral context: listing is always free, offers are labeled Sponsored, and during the early-bird phase the tool reports purchasable=false. No contradiction with 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 core action and output, followed by targeted usage and exception notes. No unnecessary 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?

For a parameterless tool with an output schema, the description covers purpose, when to call it, market context, and a special state. Nothing critical 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?

With zero parameters and 100% schema coverage, there is no parameter burden. The description adds meaning by explaining what the returned offers represent, which is sufficient for this parameterless tool.

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 clearly identifies the tool's action: listing purchasable paid add-ons (offers) that promote a listing, including what is returned (name, effect, price). This distinguishes it from siblings like create_checkout by defining it as the discovery step.

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 instructs the agent to call it before create_checkout to see what is purchasable. It also clarifies the early-bird exception (offers not purchasable yet), but does not name explicit alternatives or when-not-to-use cases beyond that.

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 provide readOnlyHint, idempotentHint, openWorldHint. The description adds specifics: matches on name/tagline/category, case-insensitive substring, returns up to 20 results, and only searches live, published tools. This goes beyond annotations and clarifies behavior.

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?

Four concise sentences, front-loaded with purpose, then specificsahan. No fluff, every sentence adds value.

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?

Given rich annotations (readOnlyHint, idempotentHint) and complete schema, the description covers scope, matching behavior, and result limit. Sufficient for an agent to invoke correctly.

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 already documents the query parameter well (100% coverage). The description adds matching details (fields searched, case-insensitivity) that clarify what the query is used for, enhancing beyond the schema's 'tool name, use-case, or category.'

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 clearly states the tool searches a catalog of live, published AI tools, matching on name, tagline, or category. It explicitly says to use this for keyword-based discovery, distinguishing it from siblings like get_tool which likely fetches a specific tool. The 'Call this first' instruction further clarifies its role.

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 description explicitly says to use this when users want to find, discover, or compare AI tools by keyword, which provides clear usage context. It doesn't explicitly exclude other tools (e.g., get_tool for known IDs), but the 'call this first' phrasing implies it's the 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?

Discloses side effects: creates a draft (not public), returns a claimUrl that the maker must visit to claim/publish, and mentions rate limiting. No contradiction with annotations (readOnlyHint false, etc.).

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 well-structured: starts with the primary purpose, then explains the draft/claim behavior, usage condition, rate limit, and optional field advice. Each sentence adds meaningful information without fluff.

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?

Covers all key aspects: creation behavior, claim process, rate limit, and parameter recommendations. The presence of an output schema means return value details need not be explained, so the description is sufficiently complete.

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?

While the schema already has descriptions for all parameters, the description adds actionable guidance on which optional fields (tagline, problemSolved, logoUrl, imageUrls) should be supplied to improve the listing, going beyond mere schema details.

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 clearly states the tool submits a new AI tool, creates a draft listing, and returns a claimUrl. It is distinct from sibling tools like search, checkout, or listing retrieval.

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?

Provides explicit when-to-use guidance: 'Call this only when a user explicitly wants to list their own tool' and a clear prohibition ('never to submit a tool on someone else's behalf without their email'). Also mentions a global rate limit of 5/hour.

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