PeerPush

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

With annotations already declaring readOnlyHint=true and openWorldHint=false, the description's main burden is to explain behavior beyond side effects. It does so by detailing the return contents: shared/unique features, pricing, platform coverage, use cases, audiences, and community engagement metrics. This is useful context not present in 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 with no filler: the first gives the core operation and scope, the second gives usage triggers and expected output. The front-loaded 'Compares 2-5 named software products' immediately answers what the tool does.

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 tool with no output schema, the description is self-sufficient. It names the exact input, the acceptable range, the trigger contexts, and what the output will look like. An agent has everything needed to invoke it correctly and interpret the response.

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% and the products parameter is already documented with min/max items and an example. The description echoes this ('2-5 named software products') rather than adding substantially new meaning, so the schema can handle the heavy lifting.

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 a specific verb ('Compares') and a clear resource ('2-5 named software products side by side'), which precisely distinguishes it from sibling tools like peerpush_find_alternative and peerpush_product_details. It leaves no ambiguity about what this tool does.

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 usage context: it is relevant when the user asks for a comparison or wants to choose among specific named products, with a concrete example ('Vercel vs Netlify'). It does not explicitly contrast with siblings like find_alternative, but the scoping to named products is clear enough.

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=true, and the description is consistent with that by describing a read-only listing operation. It adds meaningful behavioral context: results include 'currently active discount codes', an active discount code, and percentage off, which explains the tool's output scope beyond just 'list products'.

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: a concise opening statement of function, a relevance/trigger statement, and a short summary of output content. Every sentence adds useful information, and the primary action 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?

For a simple read-only list tool with no output schema, the description sufficiently covers what the tool does, when to use it, and what it returns. It could be slightly more complete by clarifying how it differs from sibling tools like peerpower_trending or peer new_launches, but that gap is already partially addressed by the relevance statement on sales and coupons.

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 input schema already documents limit and category with clear descriptions. The tool description does not need to add parameter semantics and adds no detail beyond what the schema provides; thus the baseline of 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 uses a specific verb ('Lists') and identifies the exact resource ('products on PeerPush with currently active discount codes'), making its purpose immediately clear. It also specifies the return value (active discount code and percentage off), which aligns with the tool name and title without being a tautology.

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 the tool is relevant: when the user asks about software deals, discounts, coupons, or promotions. This provides clear contextual use, but it does not explicitly mention when not to use it or name alternative sibling tools, so it stops short of a perfect 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 establish that this is read-only. The description adds transparency by stating that it returns matching products and by naming the available sort behaviors: score, recency, and trending momentum. This gives the agent a clear picture of the tool's output behavior beyond the safety 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?

Three compact sentences, each earning its place: what the tool does, when it should be used, and the kind of output it returns. The key distinction from keyword search is front-loaded and the example makes the intent instantly clear.

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 read-only discovery tool with no required parameters and no output schema, the description covers filtering behavior, sorting behavior, and usage context. It could be slightly stronger by naming the most likely sibling alternatives, but nothing essential is 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 input schema already covers all 7 parameters with descriptions and enums, so the baseline is 3. The description adds context by listing the filter dimensions and giving a concrete example, but it does not significantly deepen parameter-level understanding 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 the resource (PeerPush catalog), a specific verb (Browses), and the filtering dimensions (use case, audience, platform, pricing, category). It also contrasts discovery with keyword search, helping differentiate it from peerpush_find_product and related 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 explicitly states when the tool is relevant: when users want structured filtering, with the helpful example 'free CLI tools for developers.' It notes that it is not for keyword search, though it does not explicitly name the sibling tool that should be used instead.

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 annotations already declaring readOnlyHint=true and openWorldHint=false, the safety profile is covered, so the bar is lower. The description adds genuinely useful return behavior: outputs are 'ranked' and contain 'pricing, platforms, and use cases' plus 'community engagement metrics', which matters because there is no output schema. It omits edge-case behavior (unknown products, zero results), but that is minor for a read-only lookup.

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, no fluff — one states the function, one gives the trigger with examples, and one describes the return shape. Each sentence earns its place, and critical routing detail is front-loaded before the return-value 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?

For a moderate tool — 5 parameters, 1 required, read-only annotation — the combination of a fully documented schema and a behavior-rich description covers what an agent needs to select and call it correctly. The remaining gaps are the absence of any statement about how quantity limits apply (limit sends to 20, maximum by max related) and what happens when there are no results for an unknown product.

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 every parameter already carries its own description: product (with examples), limit (with default and range), audience, platforms, and pricingType (with enums). The description adds only a small layer of meaning by naming pricing/platforms as return fields, which loosely explains why the filters exist — but the schema mostly does the heavy lifting.

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: 'Finds alternatives and competitors to a specific software product, tool, app, or service.' Concrete examples ('alternative to Notion', 'something like Figma but free') disambiguate it from siblings like peerpush_find_product (locating the product itself) and peerpush_compare, so an agent can tell them apart without opening the schema.

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 names the trigger conditions — 'Relevant when the user asks for an alternative, replacement, or competitor to a product' — with concrete natural-language examples. It gets a 4 rather than a 5 because it does not name sibling tools or give when-not conditions (e.g., 'for comparing two specific products use peerpush_compare'), so the agent must infer the exclusions from the sibling list.

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?

The annotations already declare readOnlyHint=true and openWorldHint=false, so no side-effect warnings are needed. The description adds useful behavioral context by noting results are 'ranked by relevance and community engagement' and that matching is semantic. It doesn't go further into result format or pagination, but the annotations carry the main safety burden.

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 meaningful information with no filler. The main action and scope are front-loaded, the example follows, and the return value is stated succinctly. Every sentence 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?

For a read-only search tool, the description covers what it does, how it matches, what filters exist, when to use it, and what the results contain. The param schema is complete, the annotations cover the authorization/side-effect profile, and no output schema exists, so no additional return expectations are missing. It is not a 5 because it doesn't explicitly route away from sibling tools like discover or new_launches, but the description is still fully usable.

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 input schema already documents every parameter. The description adds context for the primary query parameter ('natural language query', 'semantic matching') and summarizes filter dimensions like pricing, platform, audience, and usage case, which aligns with the schema. This is solid but not exceptional for a fully parameter-documented tool.

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 ('searches') and resource ('PeerPush product catalog') and clearly explains the matching approach: natural language query with semantic matching and structured filters. It also includes a concrete example ('email API for transactional emails'), making the tool's purpose unambiguous and distinguishable from siblings like peerpush_compare 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 gives clear guidance on when to use the tool: when the user wants a product, tool, app, or service recommended for a specific need. It includes a relevant example, which anchors the intended use. It does not explicitly state when not to use it or mention alternatives like peerpush_find_alternative, but the use context is sufficiently 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?

Annotations already cover read-only, non-destructive behavior, lowering the burden. The description adds that results are limited to recent products and sorted by publication recency, which is helpful, but it does not describe pagination, ordering details beyond 'recently published', or what happens when no products match.

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 short sentences, each serving a purpose: what the tool does, when to use it, and what it returns. No fluff or repetition that hurts usability.

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 read-only listing tool with fully described optional parameters, the definition covers the essential invocation context. It lacks an explicit output shape, but the absence of an output schema is partially offset by the statement that the tool returns the most recently published products.

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 each parameter already has a clear description in the schema. The tool description minimally reinforces the 'days' concept but adds no substantive meaning beyond what the input schema already provides.

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 recently launched PeerPush products within a configurable number of days, which is specific and action-oriented. It does not explicitly distinguish itself from sibling tools like peerpush_trending, but the new-launch framing makes the core 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?

The description gives clear relevance cues: it should be used for newly launched products or recent software releases. It does not explicitly state when not to use it or name alternative tools, but the context provided is sufficient for typical agent 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?

The annotations already state readOnHint:true and openWorldHint:false, and the description is consistent with those, describing a safe lookup. The description adds value by naming the returned categories (pricing, platforms, use cases, target audiences, discount codes, community metrics) and that alternatives are optional. It does not present harmful edge cases or contradictions, but with annotations present, 3 is appropriate – it adds some context without needed to disclose safety itself.

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 text is compact and front-loaded: a single activation sentence, a relevance sentence, and a returns sentence. The list of output categories is a bit detailed but earns its place because there is no output schema, so the agent needs that list. No useless wording found.

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 two-parameter, read-only lookup tool, the description covers the domain (PeerPush), the search key (name/slug), the intent it serves, and the return scope. It does not mention clarifying that `includeAlternatives` defaults to true, but that is handled by the function and is not needed for selection. With no output schema, the explicit output categories are adequate.

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 function tag covers 100% of properties with their own descriptions, so the description doesn't need to repeat parameter" definitions. The description does provide product-context by listing what the result includes, but it doesn't originally explain the parameter parameters more deeply than the structure does. This lands at the minimum viable level: parameter semantics are already handled by the structure.

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 action and resource: 'Looks up a single product on PeerPush by name or slug.' It also distinguishes itself from likely siblings by emphasizing 'single product' rather than comparison, discovery, or trends. The listed output categories make the tool's scope easy to grasp.

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 a clear when-to-use rule: 'Relevant when the user brings up a specific product's pricing convinced of Sierra$'. Though it doesn't name sibling tools or provide explicit when-not-to-use guidance, the 'single product' and 'by name or slug' framing occupies excludes comparators and discovery use cases enough for an agent to choose correctly in most 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?

Annotations already communicate read-only behavior, and the description does not contradict that. It adds useful behavioral context beyond the annotations by describing what kind of results are returned: trending badges, rising upvotes, and product award winners over the chosen period. This sets expectations for a list of ranked or signal-bearing products rather than a plain catalog response.

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 short, focused sentences with zero filler: the first states the core operation, the second gives the user-intent trigger, and the third describes the expected result. It is front-loaded 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?

The tool is simple: 3 optional parameters with complete schema descriptions, no output schema, and a read-only annotation. The description covers what the output generally contains, so the agent has enough context to select and invoke the tool correctly without missing critical 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 description coverage is 100%, so the baseline is 3; the parameters themselves are already described well in the schema. The description adds little parameter-specific detail beyond restating the 'period' concept, so no extra compensation is needed but also no added value is provided.

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 a specific verb ('lists') and resource ('software products currently trending on PeerPush'), and specifies the period dimension. It also distinguishes the tool from siblings like new_launches by focusing on community momentum, trending badges, rising upvotes, and award winners, so an agent can tell sibling tools apart.

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: when the user asks what tools are popular, hot, or gaining momentum right now. It does not name explicit exclusions or alternatives, but the context is clear enough to route the agent to this tool.

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