KI Tools Heute

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

With annotations declaring readOnlyHint=false and openWorldHint=true, the description adds meaningful context by describing the human-mediated flow, the three specific error conditions (nonexistent listing, not-yet-purchasable add-on, environment misconfiguration), and clarifying the tool does NOT complete a purchase. It does not contradict any annotation, and it explains the side-effect model (creating a URL with no immediate charge) beyond what annotations provide.

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, each serving a purpose: stating the action, warning against misrepresentation, and enumerating error conditions. It's front-loaded with the primary use case and contains zero filler or redundancy. The format is ideal for an agent scanning 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?

Given the 2-parameter schema, solid annotations, and presence of an output schema, the description covers the key behavioral caveats (human-required, error cases) without redundancy. It could optionally mention retry behavior or idempotency (especially since idempotentHint=false), but this doesn't unduly hinder an agent from deciding to invoke it correctly. Overall, 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 coverage is 100% with both parameters (slug, product) fully documented including a concrete slug example and a reference to list_offers for add-on details. The baseline of 3 applies since the schema carries the weight here; the description adds no additional parameter detail, but none is needed given the thorough 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 uses a specific verb+resource construction: 'Create a Stripe checkout URL for one paid add-on on one listing, and return it for a HUMAN to open.' It clearly scopes the tool to exactly one add-on per listing and distinguishes itself from a purchase by clarifying nothing is charged until browser completion. This strongly differentiates it from sibling 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?

The description implies when to use the tool (when a human needs to complete a purchase) and clearly states behavioral constraints ('Never present this as a completed purchase'). However, it doesn't explicitly reference sibling tools like list_alternatives or list_offers for comparison, nor does it state 'use X instead when...' It has implied usage (payment flow) but lacks explicit exclusions or named alternatives.

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. It adds context about the return value (null on invalid slug) and the scope of information retrieved (full description, launch date, revenue signals, for-sale status), which are beyond the schema. It doesn't discuss permissions or rate limits, but for a read-only fetch with good annotations, this is adequate.

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 waste. The first sentence communicates purpose and input format; the second gives usage context and return behavior. Information is front-loaded with the most important details first.

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 has one parameter, a clear input format, an output schema, and strong annotations, the description covers essential aspects. It explains when to use, what it returns (including null case), and the slug format. It doesn't detail the output structure since the output schema exists, and it doesn't mention any edge cases beyond null, but for a simple retrieval tool it's complete enough.

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 a single parameter 'slug', and its description ('The tool's listing slug.') is minimal. The tool description adds crucial semantics by showing the exact format ('/tools/acme-writer' -> slug 'acme-writer') and clarifying it's the same slug returned by search_tools' toolUrl, which the schema alone doesn't convey. This exceeds 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 clearly states the tool fetches full public detail for one AI tool by slug, specifying the exact input format (e.g., '/tools/acme-writer' -> slug 'acme-writer') and its relationship to search_tools. It distinguishes itself from siblings like search_tools (which lists) and list_alternatives/list_offers (which retrieve other entities), making the purpose 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 after search_tools' and elaborates that it retrieves a tool's full description, launch date, revenue signals, and for-sale status. It also mentions the failure mode ('Returns null if the slug doesn't resolve'), giving clear guidance on when to use it and what to expect.

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, and the description adds 'live' and 'same-category' filtering context, which helps. It doesn't add much beyond annotations, but the 'live' qualifier adds temporal context.

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?

Description is two sentences, front-loaded with the core action and usage trigger, no wasted words.

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 full schema coverage, clear usage trigger, and annotations covering safety, the description is sufficient for a simple one-parameter tool. Output schema exists, so return format disclosure isn't needed.

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 description reinforces `target`'s meaning by clarifying it's the well-known slug, not a listing slug, adding value beyond the schema's 'Slug of the well-known target 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 states it 'List live, same-category alternatives to a well-known AI tool' and provides examples, distinguishing it from siblings by specifying the target is a well-known tool slug, not a catalog listing.

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 says 'Call this when a user asks...' and clarifies that `target` is a well-known tool's slug, not a listing slug, clearly differentiating from similar search_tools.

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 readOnly and idempotent annotations, the description adds meaningful behavioral context: listings are free, offers only add reach, add-ons are always labeled 'Sponsored', and early-bird offerings report purchasable=false. This gives the agent a clear model of expected behavior 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 with the core purpose, followed by key behavioral nuances and usage context. Every sentence contributes distinct information with no redundancy or 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 simple, zero-parameter read-only tool with an output schema and supportive annotations, this description is complete. It covers purpose, output contents, relation to checkout, and the special early-access condition, leaving no important gap for an agent to misuse it.

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, the description does not need to explain inputs. It adds useful output semantics by mentioning the returned fields (name, one-sentence effect, price in USD), which compensates for the empty 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 the tool lists optional paid add-ons a maker can buy, which is a specific verb+resource combination. It distinguishes itself from create_checkout by framing this as the pre-purchase discovery step, and the title reinforces the same purpose.

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 before create_checkout, providing a clear context for use. It does not explicitly list when-not-to-use cases or mention alternative sibling tools beyond create_checkout, so it falls just short of fully explicit exclusion 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?

Annotations already indicate readOnlyHint and idempotentHint, but the description adds valuable behavioral context: case-insensitive substring matching, fields searched (name, tagline, category), and the 'returns up to 20 results' limit. This exceeds annotation coverage without contradicting it.

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: first states purpose and matching behavior, second provides usage guidance and result limit. No fluff, all information front-loaded, and each clause adds value. Ideal conciseness.

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 (one parameter, read-only) and the description covers all essential aspects: what it searches, how it matches, when to use it, and the result limit. Given the high schema coverage and output schema exists, no additional context is needed.

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 has 100% coverage for the single 'query' parameter with a basic description. The tool description goes beyond that by detailing what the query matches (name, tagline, category in a case-insensitive substring), which enriches the parameter semantics. Fine for a one-param 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 explicitly states the verb 'Search' and the resource 'KI Tools Catalog of live, published AI tools'. It distinguishes from siblings like get_tool (exact retrieval) by specifying free-text query matching on name, tagline, or category, making the tool's purpose unique and clear.

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 when-to-use guidance: 'Call this first when a user asks to find, discover, or compare AI tools' — this sets priority. However, it does not explicitly name sibling alternatives (e.g., get_tool) for when not to use it, so it lacks explicit when-not/alternatives. There is no misleading guidance, but exclusions are 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?

The description discloses that the submission is not immediately public, requires a claimUrl visit for publishing, and is rate-limited globally to 5 per hour. These behaviors are not covered by annotations (which only say readOnlyHint=false, etc.), adding critical context beyond structured metadata.

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 concise, with each sentence adding distinct value: purpose, behavior, usage caveat, rate limit, and parameter guidance. No filler or repetition of schema details.

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 complexity (11 params, 5 required) and the provided output schema, the description covers purpose, workflow, caveats, and parameter guidance sufficiently. The schema covers parameter formats, and the description fills in behavioral and contextual gaps, resulting in a complete picture.

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 schema coverage is 100%, the description adds meaning for parameters like tagline, problemSolved, logoUrl, and imageUrls, explaining their impact on listing quality. It complements the schema without redundancy, adding value by motivating supply of these fields.

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 on behalf of its maker, with a specific outcome: creates a draft listing and returns a claimUrl. It clearly distinguishes from siblings like get_tool and search_tools by focusing on submission, not 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?

The description explicitly states when to use: only when a user wants to list their own tool, and warns against submitting on someone else's behalf without their email. This provides clear usage guidance and exclusions, distinguishing it from other tools like create_checkout.

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