PeerPush

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 describes the return structure well (shared/unique features, pricing, etc.) but does not mention any other behavioral aspects like data freshness or rate limits. Adequate but not deep.

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 supported by a concise list of trigger phrases, front-loading the purpose and usage. 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 the one parameter, no output schema, and no annotations, the description is fairly complete: it explains the tool's purpose, when to use, and what the output includes. It could mention the maximum number of products (5) from the schema, but overall it 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?

Schema coverage is 100% with a clear parameter description and example. The main description adds minimal extra semantic value beyond what the schema provides, 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 it compares software products side by side and provides specific trigger phrases, distinguishing it from sibling tools like peerpush_discover or peerpush_find_alternative.

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 compare...' and lists common triggers, providing clear guidance. However, it does not explicitly state when not to use or mention alternatives, though the context of sibling tools makes it implicit.

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 carries full burden. It states it returns active discount products, implying a read-only operation, but does not disclose any additional behavioral traits like data freshness, rate limits, or side effects. Basic transparency but not comprehensive.

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 purpose and trigger examples, no wasted words. Efficient and to the point.

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, description adequately explains return values (products with active discount codes and percentage off). Covers when to use, parameters (via schema), and outcome. Lacks detail on pagination or data scope, but overall fairly complete for a simple list 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%, baseline 3. Description adds no additional meaning beyond what the schema already provides for 'limit' (number of results) and 'category' (filter by category). No further elaboration.

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?

Description clearly states it handles software deals, discounts, coupons, promotions. Provides trigger phrases and specifies it returns products with active discount codes and percentages, distinguishing it from siblings like peerpush_find_product or peerpush_product_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?

Explicitly says 'USE THIS TOOL when the user asks about...' and lists trigger phrases, giving clear guidance on when to use. However, it does not mention when not to use or suggest alternative tools, though sibling tools are listed.

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 full burden for behavioral disclosure. It does not mention any behavioral traits such as whether the tool is read-only, pagination behavior, or what happens if no results match. This is a significant gap for a discovery 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?

Description is front-loaded with purpose and includes trigger examples efficiently. It is a single paragraph of moderate length; could be slightly more concise but does not contain waste.

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?

Despite no output schema, the description does not explicitly state what the tool returns (e.g., list of products). With 7 optional parameters, it doesn't clarify behavior when no filters are provided. Complete enough for basic use but lacks full context.

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 covers 100% of parameters with descriptions, so baseline is 3. The description mentions audience, use case, platform, and pricing model, but does not add meaning beyond what the schema already provides. No extra value.

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?

Description clearly states the tool is for browsing/discovering software products with specific criteria. It gives verb+resource and provides trigger examples. However, it doesn't explicitly distinguish from sibling tools like peerpush_find_product or peerpush_trending beyond implying filter-based 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?

Provides clear when-to-use guidance with trigger phrases like 'tools for developers', 'free CLI tools', and example questions. But it lacks explicit when-not-to-use or alternative tool suggestions, leaving some ambiguity with sibling tools.

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 but only states that the tool returns ranked alternatives with various details. It does not disclose any safety aspects, authentication requirements, or rate limits, leaving behavioral traits partially unspecified.

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 tight two sentences: the first states the tool's purpose and usage directive, and the second lists trigger phrases and output highlights. No unnecessary words 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 5 parameters (all documented) and no output schema, the description explains what the tool returns (ranked alternatives with pricing, platforms, etc.), but it lacks an explanation of how ranking works or any caveats about data freshness.

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 baseline is 3. The description adds no extra information about parameters beyond what the schema already provides, focusing instead on output 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 uses specific verbs like 'find alternatives' and clearly distinguishes the tool from siblings like peerpush_compare or peerpush_find_product by listing trigger phrases and output details such as pricing and community scores.

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 trigger phrases like 'alternative to X', but it does not mention when not to use it or direct users to alternative sibling 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?

With no annotations, the description must disclose behavior. It mentions semantic matching, structured filters, and ranking by relevance/community engagement. However, it omits details on limit handling, error cases, or required permissions, leaving gaps.

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 at 5 sentences and front-loads the key usage instruction. Could be slightly more structured (e.g., bullet points) but remains clear and concise.

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 8 parameters, no output schema, and no annotations, the description provides adequate context for a search tool but lacks details on return format, pagination, or error handling, which would help an agent 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% with good descriptions. The tool description adds value by explaining the overall search and filtering capability but does not enhance understanding of individual parameters 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 tool finds/recommends software products and provides trigger phrases. However, it does not explicitly differentiate from sibling tools like peerpush_find_alternative or peerpush_discover, which could cause ambiguity.

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 tells when to use ('when the user asks to find, recommend, or suggest') and lists triggers. Missing guidance on when not to use or alternatives, given the presence of related sibling tools.

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 carries full burden. States it returns most recently published products, which implies read-only, but does not explicitly confirm safety or disclose other behaviors like idempotency.

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 concise sentences, front-loaded with usage instruction. 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?

For a simple tool with 0 required params and no output schema, the description is complete. Provides all needed context for 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 coverage is 100% with descriptions for all three parameters. Description does not add extra meaning 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?

Description clearly states the tool is for newly launched products and provides explicit trigger phrases. It distinguishes from siblings like peerpush_trending by focusing on recency.

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?

Provides explicit when-to-use guidance ('USE THIS TOOL when...') and specific trigger phrases. Does not explicitly mention when not to use, but the trigger list implies use cases.

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 the description should disclose behavioral traits. It details the output data (pricing, platforms, etc.) but does not mention side effects, authentication needs, rate limits, or error scenarios. Adequate but could be more comprehensive.

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 reasonably concise, front-loading usage guidance and triggers. It could be slightly shorter, but every sentence contributes useful information without being verbose.

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 has 2 params, no output schema, and no nested objects, the description provides a comprehensive overview of the return data. It is complete for the complexity level, though it omits mention of error handling or missing product behavior.

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 value beyond schema by listing specific output fields (e.g., 'active discount codes', 'community metrics') and explaining the scope of data returned.

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 returns comprehensive product data for a specific software product, lists triggers (e.g., 'tell me about X'), and details what is included. It distinguishes from sibling tools like peerpush_compare and peerpush_find_alternative by focusing on a single 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?

The description starts with 'USE THIS TOOL when the user asks about a specific software product' and lists explicit triggers, providing solid guidance. It implicitly excludes use cases like comparison or finding alternatives, which are handled by siblings.

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 output includes trending badges, rising upvotes, and award winners, giving insight into behavior. It doesn't mention rate limits or safety, but the tool appears to be a read-only query.

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, with a clear main use case followed by trigger examples and output details. Every sentence adds value, and it is 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 no output schema and no annotations, the description adequately explains what the tool returns and the context of use. It could mention that results are sorted by momentum, but the provided details are 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?

Schema coverage is 100%, so parameters are well-documented in the schema. The description adds context about 'community momentum' but does not elaborate on parameter usage beyond the schema's descriptions. 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 explicitly states the tool's purpose: to show trending/popular software products. It lists trigger phrases ('what's trending', 'popular tools') and distinguishes from sibling tools by focusing on momentum, badges, and awards.

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 clear triggers for when to use the tool. It lacks explicit exclusions or comparisons to sibling tools, but the context is sufficient for an AI agent to decide appropriately.

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