Launchelion

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

With no annotations provided, the description carries the full burden and does excellently. It discloses the human-in-the-loop requirement, privacy (checkout email to payer, no listing-owner data), lack of session auth, and hard rule that badge verification cannot be bypassed. It also lists all error cases, making the tool's behavior fully foreseeable.

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 dense paragraph, but every sentence adds value—core action, human interaction, privacy, auth rules, and error conditions. It front-loads the primary purpose and avoids fluff. While it could be broken into sections for readability, the content is tight and well-organized.

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 there's no output schema, the description adequately explains the return value (a Stripe Checkout URL) and explicitly lists all error scenarios. It covers purpose, prerequisites, human involvement, privacy, auth, and monetization state. For a tool with this complexity, the description is comprehensive enough for an agent to invoke it 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 coverage is 100%, so baseline is 3. The description adds little beyond the schema's own parameter descriptions: it reiterates 'paid add-on' and references list_offers, but the schema already mentions the same reference and the slug format. No new semantic depth is introduced, so a baseline score 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?

The description starts with a specific verb and resource: 'Mint a Stripe Checkout URL to buy one paid add-on for a live listing.' It clearly distinguishes from siblings by focusing on checkout creation for add-ons, and even references list_offers as an alternative for product details. The scope ('live listing') and the human-payment requirement are 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?

It explicitly states when to use (buying a paid add-on for a live listing) and when not to (if listing isn't LIVE, monetization not live, etc.). It also clarifies that agents must not enter card details—a human must complete payment—and points to list_offers for further info. Error conditions are enumerated, giving clear guidance on expected failures.

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?

No annotations are provided, so the description carries the burden. It discloses that this is a read-only fetch of public data, returns null when the slug doesn't resolve, and lists the data returned (description, launch date, revenue signals, for-sale status). It does not explicitly state it has no side effects, but 'fetch' and 'public detail' strongly imply read-only. It also doesn't mention auth or rate limits, but for a simple fetch, this is reasonably transparent.

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, front-loaded with the core action, and then gives context on when to call it (after search_tools) and what it returns (including null case). Every sentence earns its place, no 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?

Given the tool's simplicity (single fetch by slug), the description covers the purpose, the input origin, the output details (full description, launch date, revenue signals, for-sale status), and error behavior (null return). It's complete for an agent to know when and how to use 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?

Schema description coverage is 100%, so baseline is 3. The description adds a helpful example for the slug parameter ('/tools/skillselion' -> 'skillselion') and clarifies it comes from search_tools' toolUrl. However, it doesn't add much beyond the schema's own definition of 'listing slug' or the site parameter's explanation. Since schema covers both, the description provides marginal additional value, hence 3.

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 'Fetch the full public detail for one AI tool by its listing slug' – a specific verb and resource. It differentiates from sibling search_tools by explicitly saying 'Call this after search_tools' and by describing it as fetching details for a single tool rather than searching. The returned fields (description, launch date, revenue signals, for-sale status) are also listed, making 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 instructs when to call this tool: 'Call this after search_tools to get a tool's full description...' This provides clear temporal guidance. It doesn't explicitly say when NOT to use it, but the sequencing and the mention of alternatives (search_tools) give adequate context. It also states the null return for invalid slugs, which helps the agent handle errors.

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?

With no annotations, the description carries the transparency burden. It mentions 'live' alternatives, implying real-time data retrieval, and adds a clarity note about slug semantics. It doesn't discuss side effects or output format, but as a read-only listing operation, this is acceptable. The description adds useful context beyond the bare name.

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 with no redundancy: purpose, when-to-use, and parameter clarification. Information is front-loaded and 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 simple 2-parameter tool with no output schema, the description covers the essential aspects: action, usage trigger, and parameter semantics. It doesn't describe return format or error conditions, but these are less critical given the tool's simplicity and the schema's completeness. The description is sufficient 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?

Schema already provides full descriptions for both parameters, so baseline is 3. The description enhances this by clarifying that 'target' is a well-known tool slug (not a listing slug) and emphasizing 'same-category,' which adds meaningful interpretation beyond the schema's minimal '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 the tool's action: 'List live, same-category alternatives to a well-known AI tool' with concrete examples. It distinguishes itself from siblings like search_tools (which is general search) by focusing specifically on alternatives for a named product.

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 states when to call it: 'Call this when a user asks 'what are the alternatives to X' or wants competitors/substitutes for a named product.' It also clarifies the target slug type, preventing misuse with launchelion listing slugs—a valuable exclusion.

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?

With no annotations, the description carries full responsibility. It discloses key behavioral traits: returns a list, includes purchasable flags, and notes the early-access state. It doesn't explicitly state it's read-only, but the listing nature and context imply no side effects.

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 sentences, each adding value: purpose, offer nature, usage, and a special state. No fluff, front-loaded with the core function.

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 no output schema, the description explains return fields (name, effect, price) and the purchasable flag. It also covers the site parameter via the schema and adjusts for the early-bird special case, making it complete for a simple listing 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% with the site parameter fully described in the schema. The description does not add any parameter-specific guidance beyond the schema, so 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?

The description clearly states the tool lists optional paid add-ons for a maker to promote their listing, with specific output fields (name, effect, price). It distinguishes itself from siblings like create_checkout (by noting it's a precursor) and list_alternatives (by focusing on paid add-ons).

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 to call this before create_checkout, indicating a specific workflow. It also explains the early-bird condition where offers are not purchasable, giving clear context for when to expect certain results.

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?

No annotations are provided, so the description carries the burden. It discloses that it searches 'live, published' tools (implying it won't return draft/unpublished tools), matching case-insensitively, and returns up to 20 results. It does not disclose pagination behavior or sorting, and it does not mention side effects (though likely read-only). This is adequate but not thorough.

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, well-structured, and front-loaded with the core action. It includes examples that enhance clarity. Slight wordiness in the enumeration of sites is avoidable, but the parenthetical of examples is helpful. It earns a 4 for being readable and mostly efficient.

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 simplicity (2 params, no output schema, no nested objects), the description covers the essential: what it searches, how matching works, and return limit. The only minor gap is the lack of explicit mention that results are read-only or any rate-limit implications, but for a search tool this is sufficient. The sibling tools are distinct enough to rely on the 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?

Schema description coverage is 100%, meaning the schema already documents both parameters. The description adds a little by clarifying that the query matches on name, tagline, or category, which enriches the meaning of 'query'. It does not add beyond that, but with full schema coverage a baseline of 3 is appropriate, and the description adds some nuance.

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 that the tool searches the launchelion network catalog of live, published AI tools, matching on name, tagline, or category with case-insensitive substring. It specifies the action and resource with enough detail to distinguish from siblings like get_tool (which likely fetches a specific tool by ID) and list_alternatives (which suggests alternatives).

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 to 'Call this first when a user asks to find, discover, or compare AI tools by keyword, use-case, or category' and gives examples. It also notes the alternative of omitting the 'site' parameter to search the whole network, implying when to use the site parameter. This provides clear contextual guidance for selection.

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?

With no annotations provided, the description fully discloses behavioral traits: it creates a draft (not yet public), returns a claimUrl for claim/publish, enforces a rate limit, copies logo and image URLs to own storage (never hotlinks), and describes fallback behaviors for missing tagline/images. No contradictions with annotations (none exist).

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 purpose, then usage constraints, then tips on parameters. Every sentence earns its place, with no wasted words 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 complexity (11 parameters, 5 required, no output schema), the description covers the essential context: the draft-claim flow, rate limiting, media handling, and parameter importance. It also states what the tool returns (claimUrl). With the schema covering field-level details, this is complete for an agent to operate effectively.

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 schema coverage is 100%, the description adds meaningful semantics beyond the schema: it explicitly advises supplying tagline, problemSolved, logoUrl, and imageUrls to avoid a 'stub' listing, explains that logo/image URLs are copied, and clarifies email usage. This is valuable context that helps the agent choose and populate parameters effectively.

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's purpose: 'Submit a new AI tool to the launchelion network on behalf of its maker. Creates a draft listing (not yet public) and returns a claimUrl...' This is a specific verb + resource + effect, and it distinctly differentiates from siblings like search_tools or get_tool by focusing on creation of a submission.

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 gives explicit when-to-use guidance: 'Call this only when a user explicitly wants to list their own tool — never to submit a tool on someone else's behalf without their email.' It also mentions the global rate limit of 5 submissions per hour, adding a clear constraint.

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