AppBoard.pro product discovery

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

No annotations exist, so the description carries the full burden. It clearly frames the operation as a read-only lookup ('Find ... alternatives'), requires a target slug rather than free-form input, and reveals a dependency on appboard_list_alternative_targets for slug resolution. It does not explicitly describe return shape, but 'find products' sufficiently implies a list of alternatives for this simple tool.

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, each earning its place: the main purpose with illustrative examples, the required input format, and the differentiation from the most similar sibling plus a fallback for slug uncertainty. No filler, no 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?

For a one-parameter, read-only lookup tool with no annotations and no output schema, this description is complete: it explains what the tool does, what input to pass, how it differs from the confusing sibling, and where to get valid slugs. Nothing essential is missing 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 description coverage is 100%: the 'tool' parameter is already documented as an AppBoard target slug with examples 'product-hunt' or 'notion'. The description reinforces this by saying 'Pass the tool's target slug' and referencing the slug list, but adds little meaning beyond the schema. 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?

The description opens with a specific verb and resource: 'Find products offered as alternatives to a tool the user ALREADY USES.' It includes concrete examples (Notion, Product Hunt, Zapier) and explicitly contrasts itself with appboard_find_alternatives, so an agent can unambiguously distinguish this tool from its siblings.

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: 'use this one when the user names the tool they want to replace.' It also names the alternative for product-listed queries and directs to appboard_list_alternative_targets when the slug is unknown. This fully covers selection and routing.

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 carries the full burden of explaining behavior. It discloses that the tool lists vocabulary terms and that each term includes the appboardUrl of its browse page, which is useful return-shape information beyond a bare 'list' statement. It does not mention pagination or error behavior, but those are minor for such a simple read-only tool.

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 fluff: it names the resource, states when to use it, and reveals the key return characteristic. The structure front-loads the core purpose before the usage note.

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 one-parameter, low-complexity tool, the description covers what it does, why to use it, and a key detail of its output. It is slightly shy of fully complete because it does not describe the full term structure beyond appboardUrl, but the schema and simplicity compensate.

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 schema already describes the kind parameter and its allowed values. The description repeats those values but adds no new parameter-level detail beyond connecting them to appboard_find_products filters, so the baseline of 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 a specific verb ('List') and resource ('controlled vocabulary for a dimension'), and enumerates the supported vocabularies. It also distinguishes the tool from product-search siblings by framing it as the way to discover the exact filter values appboard_find_products accepts.

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 tells the agent when to call this tool: to discover filter values for appboard_find_products and answer questions like 'what categories exist'. It does not exhaustively list when-not-to-use cases against all siblings, but the context is clear enough for correct 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?

No annotations provided; the description discloses some output behavior (slug, appboardUrl, alternativesPage) but does not explicitly state that it is read-only or mention any 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?

The description is a single paragraph that covers purpose, usage, and output without excessive detail.

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 provides enough context about the returned data (slug, URL fields) to understand the tool's function, though it could be slightly more explicit about the overall response structure.

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 slug parameter is clearly described as coming from appboard_find_products, providing context beyond its type.

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 finds alternatives and competitors for a specific product, which distinguishes it from other tools like product details or taxonomy browsing.

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 provides explicit usage examples ('what's a good alternative to X') and explains how to use the result, but does not explicitly contrast with sibling tools like appboard_alternatives_to.

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 full behavioral burden. It adds meaningful traits beyond the schema: results are 'ranked by real community engagement, not by advertising,' unknown filter names are an error, and the recommended recovery is to list the taxonomy vocabulary first. This gives an agent important runtime expectations.

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 dense sentences with no filler. The purpose and usage trigger are front-loaded, followed by ranking behavior, filter validation, and output fields. Every sentence 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?

Given seven optional parameters, no annotations, and no output schema, the description covers the main operational concerns: when to call, how filters are validated, error risk, ranking behavior, and the URL fields on returned products. It omits a few details like exact response shape or result-count behavior, but it is largely complete 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%, so the baseline is 3. The description adds valuable parameter semantics by tying filter values to appboard_browse_taxonomy names, warning that unknown names are errors, and advising the agent to list vocabulary first. This is genuinely useful context beyond the schema's per-parameter descriptions.

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: 'Search AppBoard for software products by need, category, use case, platform, audience or pricing model.' It names the entity type and the relevant search dimensions, and the real-world phrasing 'a free email API' makes the tool's intent unmistakable to an agent.

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 'Use this whenever the user asks what tool to use for a job,' giving clear when-to-use guidance. It does not, however, state when not to use it or contrast it with siblings like appboard_alternatives_to or appboard_trending, so it stops short of full alternative routing.

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 full behavioral burden. It discloses the result ordering ('most-covered first') and that each result includes a slug for use with appboard_alternatives_to. The action 'List' clearly implies a read-only operation, and there is no contradiction 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?

Two sentences carry the action, output characteristics, ordering, and two concrete use cases without filler. The primary purpose is front-loaded, making the tool easy to scan and understand.

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 list tool with one optional parameter, no output schema, and no annotations, the description is complete. It explains what is returned, how results are ordered, how to use the returned slug, and why an agent would call the 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 description coverage is 100%; the only parameter, limit, is already documented with 'how many targets to return (1-50, default 10)'. The description adds no parameter-level information beyond the schema, which is acceptable at this coverage level.

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 and resource: 'List the tools AppBoard has alternatives for', with a clear ordering ('most-covered first') and output purpose ('the slug to pass to appboard_alternatives_to'). This distinguishes it from sibling tools like appboard_alternatives_to and appboard_find_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 gives use cases: 'Use it to resolve a tool name to a slug, or to answer "what tools do you cover alternatives for"'. It references the related sibling appboard_alternatives_to, which helps an agent understand the downstream relationship, though it does not explicitly state 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?

With no annotations provided, the description carries the behavioral transparency burden. It implies a read-only operation ('Get everything AppBoard knows') and does not mention any side effects. However, it does not explicitly state that the tool has no side effects or is read-only, which would be more transparent. The description is otherwise clear about the data returned.

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 yet informative, consisting of two sentences that efficiently convey the tool's scope, usage context, and special link behaviors. It is well-structured without unnecessary repetition or verbosity.

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 lack of an output schema, the description sufficiently covers the tool's functionality, including the type of data returned and how to interpret certain fields (e.g., alternativesPage URL). It provides enough context for an agent to use the tool correctly without additional assumptions.

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 complete description of the 'slug' parameter ('the product's AppBoard slug, as returned by appboard_find_products'), and the tool description does not add further semantic details beyond that. Since schema coverage is 100%, the baseline of 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's purpose: retrieving comprehensive product details from AppBoard. It explicitly lists the types of information returned (e.g., pricing, platforms, alternatives) and distinguishes itself from siblings by specifying it is for a single product and should be used after appboard_find_products.

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 explicit usage guidance: 'Use after appboard_find_products when the user asks about a specific product.' It also explains how to handle returned links (taxonomy terms link to browse pages, alternativesPage is a URL to open), giving clear instructions on when and how to use the tool.

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 full burden. It explains the ranking mechanism (Product Buzz Score weighting recent upvotes, comments, follows, saves by recency), giving transparency into behavior without exposing 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?

The description is concise, two sentences, and front-loaded with the main action. It efficiently conveys purpose, usage, and ranking logic without unnecessary 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?

Given there is no output schema, the description adequately communicates what the tool returns (ranked products). It could mention output format, but for a simple list tool, the context is sufficient.

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 covers both parameters with clear descriptions (limit as count, period as time window). The description doesn't add extra meaning beyond what's in the schema, so a baseline score of 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 products gaining community momentum. It is distinct from sibling tools like appboard_find_products or appboard_product_details, which focus on searching or retrieving details.

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 says to use this for 'what's new/hot/trending' questions, providing clear when-to-use guidance. Although it doesn't mention when not to use it, the explicit instruction is sufficient.

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