Lanzamientos IA

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

Beyond annotations, the description discloses key behavioral traits: nothing is charged until a human completes checkout, and the return is a URL for a human to open. It also enumerates error scenarios. This adds significant context beyond what annotations provide and does not contradict them.

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 a critical usage caveat and error conditions. Every sentence earns its place with no 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 output schema and annotations, the description covers all necessary context: what the tool does, the human-in-the-loop requirement, non-charge behavior, and failure modes. It is complete for a moderate-complexity tool with good structured metadata.

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 fully documents both parameters with descriptions and enums, so the baseline is 3. The description's mention of 'one paid add-on on one listing' loosely reinforces the mapping but does not add new syntax or formatting details 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 the action ('Create a Stripe checkout URL') and the specific scope ('one paid add-on on one listing'), distinguishing it from the sibling tools which handle listing/search/submission. The purpose is unambiguous and specific.

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 strong usage guidance by stating that the link must be given to a human and must never be presented as a completed purchase. It also lists error conditions when the tool should not be expected to succeed. However, it does not explicitly name an alternative tool (though list_offers is referenced in the schema).

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 readOnly and idempotent, and the description adds useful behavioral details: it mentions the tool returns null for unresolvable slugs and lists the types of information (full description, launch date, revenue signals, for-sale status). This goes beyond the annotations without contradicting them.

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 core purpose, then the usage context and an edge case. 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?

With an output schema present, the description need not detail return structure. It covers purpose, usage guidance, parameter extraction, and error behavior (null return), making it fully self-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 coverage is 100% but the slug description is minimal. The description adds a precise example (e.g., '/tools/acme-writer' -> 'acme-writer'), clarifying exactly what format is expected, which is valuable beyond the schema's generic 'tool's listing slug'.

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 its slug, with a concrete example of slug extraction. It explicitly distinguishes from search_tools (which lists) and other siblings, making its 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?

It instructs to call this after search_tools, establishing a clear workflow. It also notes the null return for invalid slugs, which helps the agent decide when to use it. However, it doesn't explicitly mention when not to use it or provide alternatives beyond the implied sequence.

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, idempotentHint, and openWorldHint=false. The description adds 'live' (real-time data) and 'same-category' filtering, which are behavioral traits beyond the annotations. No contradictions 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?

The description is two sentences, front-loaded with the main action, and every sentence contributes meaning. Includes examples and usage context without 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 simple single-parameter schema, clear annotations, and presence of output schema, the description covers all necessary aspects: what it does, when to use it, and a key parameter nuance. It is complete for the tool's 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 has 100% coverage for the single `target` parameter. The description adds extra semantic value by emphasizing the target must be a well-known tool's slug and not a listing slug, which is not fully captured in the schema description.

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: 'List live, same-category alternatives to a well-known AI tool' with specific examples. It distinguishes itself from siblings like get_tool and search_tools by focusing on alternatives to well-known 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?

Explicitly provides when to call: 'Call this when a user asks what are the alternatives to X or wants competitors/substitutes for a named product.' Also adds a crucial restriction that `target` is a well-known tool's slug, not a listing slug from the catalog.

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 meaningful behavior: listing is free, add-ons only increase reach, results are always labeled 'Sponsored', and during early access offers are marked purchasable=false. This gives the agent important business-context and edge-case knowledge that annotations alone do not convey.

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 and front-loaded with the core purpose. Every sentence earns its place: product definition, business context, usage guidance, and an early-access caveat. There is no filler or repetition of schema/annotation information.

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 zero-parameter, read-only list tool with an output schema, the description is complete. It names the returned fields, clarifies the economic model, connects to checkout, and exposes the early-access behavior. No important context appears 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?

The tool has zero parameters and the input schema is empty, so there is nothing for the description to explain. With 0 params, the baseline is 4, and the description appropriately focuses on output semantics rather than parameter 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 opens with a specific verb and resource: 'List the optional paid add-ons a maker can buy to promote their listing.' It clearly distinguishes these purchasable add-ons from the always-free listing and from the sibling create_checkout tool, so the tool's purpose 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 gives clear context with 'Call this to see what's purchasable before create_checkout,' explicitly situating it in the purchase workflow. It does not list exclusions or alternatives, but this sequencing guidance is enough for an agent to know when to invoke 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 indicate read-only and idempotent behavior. The description adds meaningful context about case-insensitive substring matching, the fields searched, and the result cap of 20, which goes beyond the structured 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 concise and well-structured: one sentence explains the search behavior and fields, and a second provides usage timing and output limit. No unnecessary details or repetition.

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, one required parameter, and the presence of an output schema, the description covers purpose, usage context, matching behavior, and result limit. It is fully sufficient without further elaboration.

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 provides a clear description of the query parameter, so the baseline is 3. The description adds extra semantics by specifying what fields the query matches against and the case-insensitive substring behavior, improving parameter understanding.

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 specific catalog by free-text query, matching on tool name, tagline, or category. It distinguishes itself from siblings by being a search-first approach to discovering/working with 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 explicitly says to call this tool first when users ask to find, discover, or compare AI tools by keyword, use-case, or category. It provides clear context but does not explicitly mention alternatives or when not 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?

Despite basic annotations (readOnlyHint=false, destructiveHint=false), the description adds substantial behavioral detail: creates a draft rather than a public listing, returns a maker-claim URL, globally rate-limited to 5/hour, and copies logo/images to its own storage rather than hotlinking. These are meaningful operational facts beyond 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 front-loaded with the core action, then provides the one-sentence claim flow, usage guardrail, rate limit, and quality guidance. Every sentence earns its place with no 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 11 parameters, 5 required fields, and an output schema, the description richly covers the behavioral context: draft vs. public, claimUrl return, consent requirement, rate limiting, and optional-parameter value. Nothing critical is omitted for an agent to decide when and how to call this 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%, so the baseline is 3. The description adds value by emphasizing optional fields ('Supply tagline, problemSolved, logoUrl and imageUrls whenever you can') and their impact on listing quality ('real product page rather than a stub'), which guides an agent in collecting useful parameters beyond just their schema definitions.

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 Lanzamientos IA on behalf of its maker' and immediately clarifies the draft/claim flow. It clearly distinguishes from siblings like create_checkout and search_tools by explaining this tool creates a non-public draft with a claimUrl.

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 usage direction: '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.' This tells the agent when to use it, when not to use it, and a critical consent/email limitation.

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