Beste KI Tools

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

Adds significant value beyond annotations by disclosing that no charge occurs until a human completes checkout, and enumerating specific error scenarios (listing not found, add-on not purchasable, payments not configured). This is exactly the kind of side-effect and failure-mode transparency that annotations do not capture.

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 tight sentences: purpose, critical caveat, and error conditions. Every sentence earns its place with no fluff. Front-loaded with the core function, then important behavioral warnings, then edge cases.

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 complexity (side-effectful, external payment integration) and the presence of an output schema, the description thoroughly covers purpose, side effects, and failure modes. No obvious missing information for an agent to use this safely.

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 baseline 3 applies. The description does not add parameter-specific details beyond what the schema already documents (slug and product). The phrase 'one paid add-on' and 'one listing' hints at cardinality but no additional 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?

Clearly states the tool creates a Stripe checkout URL for one paid add-on on one listing and returns it for a human to open. The verb 'create' and resource are specific, and the scope is unambiguous. It naturally differentiates from sibling tools like list_offers 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?

Provides clear context: the link is for a human, not to be presented as a completed purchase, and error conditions are listed. However, it does not explicitly name alternatives or state 'use when' versus other tools, though the 'for a HUMAN to open' and error cases imply appropriate usage.

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=true and idempotentHint=true, indicating a safe read operation. The description adds useful behavioral context beyond annotations: it discloses that the tool returns null for invalid slugs, indicating no error throw, and specifies the type of data returned (description, launch date, revenue signals, for-sale status). This is more than the annotations alone provide, hence a 4.

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 sentences, tightly written, and front-loaded with the core action and input. Every phrase adds value: the slug format example, the recommended call sequence, and the null return behavior. There is no verbose or redundant text.

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 single parameter, high schema coverage, the presence of an output schema (automatically defining return structure), and read-only annotations, the description covers all necessary context: what to call, when to call, what to expect in return, and the null edge case. There is no gap that would hinder 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 has 100% coverage of the parameter 'slug' with its description 'The tool's listing slug.' The description reinforces this by explaining how the slug is derived (from search_tools' toolUrl) and gives an example. This matches the baseline of 3 for high schema coverage, and the added example provides slight extra value without overstepping.

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 fetches full public detail for one AI tool by slug, distinguishing it from siblings. It specifies the verb ('fetch'), the resource ('one AI tool'), and the precise input (slug from search_tools' toolUrl). It also provides an example of slug format, eliminating ambiguity.

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 call this after search_tools to obtain full description, launch date, revenue signals, and for-sale status. It clarifies that the slug comes from search_tools' toolUrl, and notes that it returns null for unresolvable slugs, which guides the agent on when and how 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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds meaningful behavioral context beyond the schema by noting results are 'live', 'same-category', and that the target must be a well-known tool's slug rather than 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?

Two tight sentences: the first states the operation with examples, the second gives the exact user-intent trigger and a critical parameter clarification. No filler; 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-parameter tool with a full output schema, strong annotations, and clear usage guidance, the description is complete. It covers what the tool does, when to use it, what the target parameter means, and the special constraint about slugs.

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 parameter already has an example, so the baseline is 3. The description adds value by explicitly warning that `target` is the well-known tool's slug, not a listing slug from this catalog, which prevents a likely misuse.

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 a specific verb and resource: 'List live, same-category alternatives to a well-known AI tool'. It also gives concrete examples and distinguishes itself from sibling tools like search_tools by clarifying that the input is a well-known tool's slug, not a listing slug from the catalog.

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 states when to use the tool: 'Call this when a user asks “what are the alternatives to X” or wants competitors/substitutes for a named product.' It provides the trigger context but does not explicitly name alternative tools or state when not to use this tool versus search_tools, so it stops short of a full 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 indicate read-only and idempotent behavior, and the description adds valuable context: listing is always free, offers are labeled 'Sponsored', and early-bird mode returns purchasable=false. This goes beyond the structured metadata and helps the agent understand expected response semantics.

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 long, front-loaded with the main purpose, and every sentence adds meaningful information: what is listed, what is returned, and the early-access behavior. No filler or redundancy.

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 absence of parameters, the presence of an output schema, and strong annotations, the description fully covers the situational nuance (early access, purchasable flag, Sponsored labeling). Nothing important is missing for an agent to invoke and interpret this tool 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?

The tool has zero parameters, so the input schema carries no burden. The description appropriately focuses on the returned data (name, effect, price, purchasable flag) rather than input semantics, which is sufficient for a parameterless list operation.

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 a specific verb ('List') and a clear resource ('optional paid add-ons (offers)'), with details about name, effect, and price. It distinguishes itself from siblings like create_checkout by explicitly framing list_offers as the lookup step before checkout.

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,' giving a clear when-to-use directive. It also explains the early-access caveat, telling the agent that offers may not be purchasable yet and will show purchasable=false, which prevents 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 declare readOnlyHint=true and idempotentHint=true, so the description doesn't need to restate that. It adds valuable behavioral details: case-insensitive substring matching on three fields, and a result cap of 20. This goes beyond annotations but doesn't cover pagination or sorting, which is minor given the output schema and simplicity.

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: the first covers purpose and matching scope, the second provides usage guidance and examples. No filler, front-loaded with the core purpose, and 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 the simple tool (1 parameter, no nested objects) and presence of an output schema, the description fully covers what the agent needs: what it searches, how it matches, the usage context, and result limit. It is complete for its complexity level.

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 describes query as 'Free-text search term', but the description enriches it with what it matches against (name, tagline, category) and the case-insensitive nature. With 100% schema coverage, baseline is 3, but the added semantics about matching logic earns a 4.

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 the 'Beste KI Tools catalog' of live, published AI tools by free-text query, matching on name, tagline, or category. This specific verb+resource+scope distinguishes it from siblings like get_tool (exact retrieval) and list_alternatives (alternative discovery), and includes concrete examples.

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 instructs to 'Call this first when a user asks to find, discover, or compare AI tools by keyword, use-case, or category', providing clear guidance on when to use. It also gives example queries ('agents', 'writing assistants'). The mention of 'first' implies it's the entry point, aligning with sibling tools for follow-ups, though it doesn't enumerate exclusions. Still, the explicit directive is strong.

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 adds substantial behavioral detail beyond annotations: listings are drafts, not public; a claimUrl is returned; the tool is globally rate-limited to 5 submissions per hour; logo/image URLs are copied to storage. This gives the agent important operational expectations not conveyed by the readOnlyHint/idempotentHint/destructiveHint flags.

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 purpose and result, followed by usage constraints and field guidance. No filler or repetition; 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?

For a tool with 11 parameters and an output schema, the description covers the most decision-critical context: draft vs public state, claim flow, rate limit, ownership/consent rule, and the role of optional fields. It is complete enough for an agent to invoke correctly without further documentation.

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 already 100%, so the baseline is 3. The description adds meaningful parameter guidance by emphasizing tagline, problemSolved, logoUrl, and imageUrls, explaining why they matter, and noting fallback behavior (e.g., truncated description, placeholder card) that is not in the schema itself.

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 action ('Submit a new AI tool to Beste KI Tools on behalf of its maker') and the concrete result ('Creates a draft listing... returns a claimUrl'). It clearly differentiates this tool from read/list/search siblings by describing the submission and claimed-publish workflow.

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?

Explicit when-to-use guidance is provided: 'Call this only when a user explicitly wants to list their own tool.' It also states a boundary condition ('never to submit a tool on someone else's behalf without their email'), which helps the agent avoid misuse despite there being no sibling tool with overlapping submission behavior.

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