Launchpad HQ

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

The annotations already declare that this is a read-only, idempotent, non-destructive, closed-world operation. The description adds useful context about what is compared (plans, API pricing, usage limits, platforms) and that each result carries an as_of date and source, though it does not elaborate further on output 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?

The description is a single front-loaded sentence that immediately states the usage condition and the comparison dimensions. There is no wasted wording.

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 one-parameter input, existing output schema, and rich annotations, the description is nearly complete: it explains when to use the tool and what comparison dimensions are returned. The remaining gap is that it does not help the agent choose between this tool and its siblings.

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 the single platforms parameter and its 2-4 item constraint are already fully documented in the schema. The description merely restates '2 to 4 specific AI tools' and adds no format or interpretation 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 names a specific verb (compare) and resource (AI tools) and states the scope of 2 to 4 tools side by side. It does not explicitly distinguish itself from the sibling tools such as find_free_alternatives or get_pricing_facts, so it falls short of a 5.

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 gives a clear triggering condition: use when the user wants to compare 2 to 4 specific AI tools side by side. It does not state when not to use the tool or explicitly name alternatives among the siblings, so it is clear but incomplete for a 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 declare read-only, idempotent, non-destructive, closed-world behavior. The description adds real value beyond that by disclosing ranking logic ('verified free tiers rank first') and a labeling rule ('catalog-only matches are labeled unverified'), which tells the agent how to interpret results.

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 dense sentences with no filler: the trigger condition leads, followed by result-ranking semantics. Every clause carries information the agent can act on.

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 and rich annotations, the description need not explain return values, and it correctly focuses on when to call and how results are ordered. Only the absence of sibling routing and any note on the limit parameter keeps it from a 5.

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 both the platform slug and the limit (1-10, default 5) are already documented in the schema. The description only vaguely gestures at the platform parameter via 'a specific AI tool' and adds no format or syntax details, so baseline 3 applies.

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?

States a specific action (find alternatives) with a clear qualifier (free or freemium) and a scoped target (a specific AI tool). It is distinguishable from siblings like compare and get_pricing_facts, though it does not explicitly name them.

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 opening clause 'Use this when the user wants a free or freemium alternative to a specific AI tool' gives explicit usage context. It stops short of stating when to prefer a sibling (e.g. compare or search_tools) instead, so no exclusions are provided.

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/idempotent/non-destructive/closed-world, and the description adds genuinely new behavioral context: verified facts take precedence and unverified text is labeled catalog_only. That labeling contract is useful and not derivable from structured fields, though rate limits or match-failure behavior remain unstated.

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, both earning their place: the first is the trigger, the second is the result-precedence rule. Front-loaded with the usage condition and 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?

With an output schema present and annotations covering the safety profile, the description only needs to convey trigger and sourcing semantics, which it does. Minor gaps: behavior when the platform slug is unknown or ambiguous is not addressed.

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 single parameter already documents that it accepts a Launchpad HQ slug or name. The description adds no format or normalization guidance beyond that, so this is baseline-appropriate rather than enriched.

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?

Names a specific resource (pricing facts / free tier) and scopes it to a single AI tool, which implicitly separates it from the sibling 'compare' tool. An agent can tell this is a single-entity lookup rather than a comparison or search.

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?

Gives an explicit trigger: 'Use this when the user asks what one AI tool costs or what its free tier includes.' The 'one AI tool' framing contrasts with compare/find_free_alternatives, though those siblings are never named and no when-not condition is 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?

Annotations already establish read-only, idempotent, closed-world behavior, so the safety profile is covered. The description adds genuine value by disclosing the result content ('Launchpad HQ catalog matches with dated, sourced free-tier facts where available'), telling the agent the data is curated and provenance-tagged rather than scraped live.

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, no waste, with the usage trigger front-loaded before the return-value note. Every clause carries information the agent can act on.

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, annotations covering safety, and 80% schema coverage, the description only needs to supply routing and return-shape context, which it does. Minor gaps remain: no guidance on what happens when nothing matches or on how 'free-tier facts where available' may be absent.

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 80%, so the schema already documents every parameter including enums and the limit range. The description only echoes the parameter names ('budget, skill level or platform') without adding filtering or ranking nuances, which is the baseline expectation.

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 verb+resource ('find AI tools for a task') plus the qualifying dimensions (budget, skill level, platform), so the agent knows exactly what the tool returns. It does not name or differentiate from siblings like find_free_alternatives, which overlaps on the 'free' axis, so it falls short of a 5.

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 trigger condition is stated ('use this when the user wants to find AI tools for a task'), which is clear enough for the primary case. However, no exclusions or alternatives are named — the agent is left to decide on its own between this, find_free_alternatives, and get_pricing_facts.

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