PeerPush

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

With no annotations, the description fully discloses that the tool returns a structured comparison with shared/unique features, pricing, platform coverage, use cases, audiences, and community metrics, providing clear behavioral 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 concise sentences: purpose and when to use, trigger examples, output description. No wasted words, front-loaded with key 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?

Given one parameter with full schema, no output schema, and sibling tools, the description covers when to use, triggers, and output components. Lacks mention of limitations but is adequate for a comparison 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 baseline is 3. The description adds value by explaining what the comparison includes (features, pricing, etc.), which helps the agent understand the output beyond the input 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 verb 'compare' and the resource 'software products side by side', and distinguishes from sibling tools like peerpush_product_details and peerpush_find_product by specifying the comparison intent.

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 lists trigger phrases ('compare X vs Y', 'X or Y', etc.) and states when to use the tool. It does not mention exclusions or alternatives, but the sibling context implies other tools for different intents.

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, so description must disclose behavior. It states returns products with discount codes and percentage off, but lacks details on auth, rate limits, or non-destructive nature.

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 concise and front-loaded with a clear usage instruction. No 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 no output schema, the description explains return value. It covers the tool's purpose and basic behavior, but could include more details on sorting, no-deals case, or rate limits.

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 descriptions for both parameters. The description adds minimal additional meaning beyond the schema, so 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 is for software deals, discounts, etc., and provides trigger examples. However, it doesn't explicitly distinguish from siblings like peerpush_find_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 says 'USE THIS TOOL when...' and lists trigger phrases, offering good guidance. No exclusions or alternatives mentioned.

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 full burden for behavioral disclosure. It mentions combining filters and answering complex queries but does not disclose side effects, read-only nature, rate limits, or authentication requirements. This is a significant gap for a tool with no 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 a single paragraph that gets to the point quickly with a clear directive, triggers, and examples. It could be slightly more structured (e.g., bullet points) but is concise and front-loaded.

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 (7 parameters, no output schema or annotations), the description provides good usage context but lacks details on return format, pagination, error handling, or behavior beyond filtering. It is adequate but not comprehensive.

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 input schema already describes each parameter. The description adds context about combining filters and provides example queries, but does not add detailed semantics beyond the schema. 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 is for browsing or discovering software products by criteria like audience, use case, platform, or pricing. It gives example triggers and query types, making the purpose distinct from siblings like peerpush_find_product or peerpush_trending.

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 TOOL when the user wants to browse or discover software products by specific criteria' and lists example triggers. It does not explicitly say when not to use it, but the context is clear enough for an agent to decide.

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 of behavioral disclosure. It does not state whether this is a read-only operation, whether it queries external sources, requires authentication, or has rate limits. The description focuses on what it does but not how it behaves.

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 'USE THIS TOOL', covering triggers and output with zero wasted words. 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 no output schema, the description explains return values (ranked alternatives with pricing, etc.). The tool has 5 parameters and no nested objects. Missing behavioral info somewhat reduces completeness, but overall it is adequate for an agent to understand usage.

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 schema already documents all five parameters. The description does not add significant meaning beyond the schema; it mentions the output format but not how parameters influence results. 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 finds alternatives, replacements, or competitors for software products, listing trigger phrases and specifying output (ranked alternatives with pricing, platforms, use cases, community engagement scores). It distinguishes from siblings like peerpush_compare and peerpush_discover by focusing specifically on 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 says 'USE THIS TOOL when the user asks for alternatives...' with concrete trigger phrases (e.g., 'alternative to X', 'something like X'). It does not explicitly exclude other use cases or mention when to use sibling tools, but the context is clear.

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. It discloses key behaviors: natural language search with semantic matching, structured filters, and ranking by relevance and community engagement. It does not mention potential side effects, auth requirements, or rate limits, but these are less critical for a search 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?

The description is concise with two sentences: the first is a clear instruction for when to use the tool, and the second describes its features. Every sentence is necessary and 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?

The description adequately covers purpose and key features, but lacks details on return format, pagination, error handling, or what happens with empty results. Given the tool has 8 parameters and no output schema, more information about the response structure would be beneficial for an agent.

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% (all 8 parameters described in schema). The tool description adds value by explaining the semantic matching behavior of the 'query' parameter and summarizing the filter capabilities, going beyond the schema's individual 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 clearly states the tool's purpose: to find, recommend, or suggest software products. It includes specific trigger phrases and distinguishes from siblings by its broad product search scope with semantic matching and structured filters.

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 provides usage triggers ('best tool for X', 'recommend a X') and context for when to use the tool. However, it lacks explicit guidance on when not to use it or differentiation from sibling tools like 'peerpush_find_alternative' or 'peerpush_discover'.

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 must convey behavior. It states it returns 'the most recently published products on PeerPush', implying a read-only, non-destructive operation. No contradictions. However, it could mention if results are sorted or if there are any side effects, but it's 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?

The description is concise (two sentences) and front-loaded with purpose and triggers. Every sentence adds value; no wasted words. The trigger list is helpful and compact.

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 (3 parameters, no nested objects, no output schema), the description is complete. It explains what the tool does, when to use it, and what it returns. No gaps for an AI agent.

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 parameters are already well-documented in the schema. The description adds no additional meaning beyond the schema; it only mentions 'recently published' which corresponds to the 'days' parameter. 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 clearly states it is for newly launched products, recent releases, and what's new in software/tools, with specific trigger phrases. It distinguishes itself from sibling tools like peerpush_trending, peerpush_deals, etc., by focusing on the 'newly launched' aspect.

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 'USE THIS TOOL when the user asks about newly launched products...' and provides a comprehensive list of trigger phrases (e.g., 'new tools', 'just launched', 'recent releases'). This gives clear when-to-use guidance, and the sibling tool names imply 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?

No annotations are provided, so the description carries full burden. It lists what the tool returns (pricing, platforms, etc.) but does not explicitly state that it is read-only or disclose any side effects. The absence of behavioral notes is acceptable for a details tool but leaves minor ambiguity.

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 efficient, using two sentences to convey purpose, triggers, and output. The trigger examples are slightly verbose but valuable for an AI agent. No unnecessary content.

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 and no annotations, the description covers the tool's inputs and outputs well. It explains what triggers to use and what data is returned. It does not address errors or limitations, but the complexity is low.

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 input schema provides 100% coverage with descriptions, but the description adds value by explaining when to set includeUpdates (if user asks about development activity) and notes the default for includeAlternatives. It also describes the response contents, which are not in 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 tool's purpose: retrieving details about a specific software product. It explicitly lists trigger phrases and differentiates from sibling tools by focusing on individual product queries versus comparison, deals, or discovery.

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 with trigger examples and states when to use this tool. It does not explicitly contrast with sibling tools, but the sibling names (e.g., peerpush_compare, peerpush_deals) provide implicit differentiation, making the intent clear.

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 full behavioral burden. It discloses what the return data includes (trending badges, rising upvotes, award winners) but does not mention read-only nature, rate limits, authentication needs, or other behavioral traits. This is adequate but not rich.

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 usage directive, and every sentence adds value. 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?

Given that there are no annotations, no output schema, and three optional parameters, the description covers the essential context: when to use, what triggers, and what the results contain. It could mention output format or pagination, but for a simple trending tool it is sufficiently complete.

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 descriptions for all three parameters. The description does not add any additional meaning or clarifications beyond what the schema provides, 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 identifies the tool's purpose with a specific verb ('returns trending software products') and resource. It lists concrete trigger phrases ('what's trending', 'popular tools') and the scope ('recent community momentum'), making it easy to distinguish from sibling tools like peerpush_discover or peerpush_new_launches.

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 this tool with a bold directive and multiple trigger examples. However, it does not mention when not to use it or provide alternatives from the sibling tools, so it loses some points for completeness.

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