KI Alternativen

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

Beyond the annotations, the description adds essential behavioral details: checkout is not completed until a person opens the URL in a browser, no charge occurs during creation, and predictable errors occur when the listing is missing, the add-on is free early access, or payments are not configured. This gives the agent an accurate model of side effects without contradicting the 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?

The description is compact and front-loaded: the first sentence states the outcome, the second sets the buyer-authorization expectation, and the third lists failure cases. Every sentence adds distinct value with 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?

Given the required parameters, an output schema, and the relevant annotations, the description is complete enough for an agent to call the tool correctly. It explains the main caveat of creating a checkout link, when it errors, and that the resulting URL is for a human to complete, leaving no critical runtime surprise unexplained.

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 description does not elaborate on the parameters themselves, but the input schema fully documents both slug and product, including the product enum and pointer to list_offers. Since schema description coverage is 100%, the description adds no additional parameter semantics; the schema carries that weight.

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 action and resource: creating a Stripe checkout URL for one paid add-on on one listing. It also clarifies the intended recipient is a human, which distinguishes this tool from purchase-completion or listing-management siblings.

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 clearly conveys when the tool is appropriate: whenever a checkout link for a paid add-on is needed before a human completes payment. It also gives important negative context by stating that this is not a completed purchase and will error in non-purchasable cases. It does not explicitly route the agent to sibling tools or name alternatives, though the schema pointing to list_offers partially fills that gap.

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?

Beyond the readOnlyHint and idempotentHint annotations, the description adds the 'live' qualifier and clarifies that the input is a well-known tool slug rather than a catalog slug, preventing a common misuse. No contradictory behavior is stated, and the added context is relevant to how the tool behaves.

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 concise sentences front-load the core action ('List live, same-category alternatives') and then provide usage and parameter guidance without any fluff. Every word earns its place, making it easy for the agent to parse quickly.

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 a one-parameter schema, full schema description coverage, a documented output schema, and three annotations, the description is sufficiently complete. The explicit usage trigger and parameter disambiguation cover everything needed for correct invocation.

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 schema already documents 'target' with full coverage, but the description adds crucial disambiguation: the slug must be a well-known tool's slug, not a listing slug from this catalog. This nuance is not expressed in the schema and adds real semantic value.

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 uses the verb 'list' with the resource 'alternatives to a well-known AI tool' and clearly distinguishes itself from catalog-listing tools by noting the target is not a listing slug. This makes the tool's purpose unmistakable and differentiates it from siblings like get_tool or search_tools.

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 includes an explicit trigger phrase: 'Call this when a user asks...' and clarifies that it covers competitors/substitutes, giving the AI agent clear conditions for selecting this tool. This goes beyond generic context and provides actionable usage 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?

The description goes beyond annotations by noting it returns purchasable flag (readOnlyHint) which is extra context. The description also discloses early-bird status effect, which is behavioral transparency. The description says what is the behavior (purchasable) but the description already covers that, but the description adds the early-bird context, which is valuable. No contradiction found.

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 a single paragraph with three sentences, each packed with relevant information: what the tool lists, that it's free and optional, and the early-bird caveat. There is no fluff or repetition, making it appropriately sized and front-loaded with the core 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 has no parameters, annotations are provided (readOnlyHint, idempotentHint), and there is an output schema (though not detailed here). The description covers the key contextual points: that offers are optional add-ons, their purpose, and the early-access limitation. This is sufficient for the agent to use the tool correctly, and the output schema likely details the response structure.

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 schema has zero parameters, so there is nothing for the description to explain. The description still adds meaning by describing what the tool returns (name, effect, price, purchasable flag), which is useful for the agent's decision. With no parameters, the schema coverage is 100% coverage, but the schema coverage is, but the description's description coverage is high, but the schema coverage is the same, but the output schema, but the schema coverage is the same as the same, but the same as the same as the same, so the description adds value 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 clearly states it lists optional paid add-ons (offers), specifies the details (name, effect, price), and distinguishes it from alternatives by noting it is the precursor to create_checkout. The verb 'List' with a specific resource (paid add-ons) 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 description explicitly says 'Call this to see what's purchasable before create_checkout', providing clear when-to-use guidance. It also explains the purpose (promote listings) and clarifies that during early access, offers are not purchasable, which is a 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?

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.