Uneed

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds information about the return format (document shape for deep research) but does not disclose any additional behavioral traits such as side effects, rate limits, or authorization needs.

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 concise sentences with front-loaded action ('Use this to retrieve...') and 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 single-parameter tool with good annotations, the description covers the purpose, usage context, and relationship to siblings. It references 'deep research expects' without elaboration, but this is likely acceptable given the tool's simplicity.

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 schema already describes the 'id' parameter as 'Product slug returned by search'. The description merely restates this (by its id, the product slug) without adding new semantics.

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 retrieves a full product profile by its slug, and distinguishes from sibling 'search' which returns summaries. It also notes it returns the same data as 'get_product' but in a specific document shape for deep research.

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 context that the tool is used after 'search', mentioning the id is the product slug from search. It references 'get_product' as an alternative but does not explicitly state when to choose one over the other, nor does it list exclusions.

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?

Adds significant context beyond annotations: ranking by weighted tag overlap, shared tags explaining results, and that paid placement does not affect ranking. No contradiction.

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?

Four sentences, front-loaded with purpose, each sentence adds value without redundancy.

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?

Complete for a read-only tool with two parameters: explains purpose, ranking, paid policy, and suggests next step (use get_product). No output schema needed.

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 50% (slug described, limit not). Description does not add parameter details beyond what's in schema, failing to compensate for low coverage.

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?

Clearly states the tool finds alternatives or competitors to a product on uneed.best, with a specific verb ('get') and resource ('alternatives'), distinguishing it from siblings like search_products or get_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 specifies when to use (user wants alternatives/competitors) and mentions ranking details; lacks explicit when-not-to-use but 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?

The description goes beyond the readOnlyHint annotation by disclosing important interpretive details: `premium` is a disclosure for paid placement, not a quality signal; `url` may be an affiliate link while `uneed_url` is neutral; `domain_rating` can be null if not measured. These behavioral nuances help the agent correctly rank and use the data without misleading users.

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 front-loaded with the core purpose, followed by a compact list of return fields, then targeted disclaimers, and finally a cross-reference to sibling tools. Every sentence earns its place without redundancy. It is dense but well-structured and easy to scan.

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 fully covers the output semantics (fields, null handling, link trust), the input source (how to get slugs), and the tool's read-only nature (consistent with annotations). For a single-parameter read tool with no output schema, this is complete and leaves no critical gaps.

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 documents the single `slug` parameter with an example ("resend"). The description adds context on how to acquire a valid slug by referencing sibling tools, which is beyond raw syntax. Since schema coverage is 100% and the only param is clear, a 4 is appropriate for the added guidance.

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: "Use this when you have a product slug and need its full public profile on uneed.best." It enumerates the specific data returned (description, website URL, category, tags, pricing, stats, etc.), distinguishing it from sibling tools like get_alternatives and search_products. The verb 'get' plus resource 'product' is explicit and 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?

It explicitly states when to use the tool: "Use this when you have a product slug" and provides clear guidance on how to obtain slugs: "Find slugs with search_products, get_trending, get_recent_launches or get_alternatives." This distinguishes it from search/explore tools and gives actionable context.

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?

Beyond annotations (readOnlyHint, destructiveHint), description adds that ordering is by launch date with no paid placement influence, providing useful behavioral context.

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?

Four sentences, all essential, front-loaded with usage instruction. No redundant 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?

Covers ordering, filtering, and relation to get_trending. No output schema but description is sufficient for this list tool; minor gap on return format details.

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 has 67% coverage; description adds that category can be slug or exact name and references list_categories, enhancing parameter understanding beyond 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?

Description clearly states 'Lists the most recent product launches on uneed.best, newest first' and distinguishes from sibling tool get_trending by mentioning combination usage.

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 when the user asks what is new or what launched recently' and provides context for filtering and when to combine with get_trending.

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 destructiveHint=false. The description adds valuable behavioral context: only published top-level reviews returned, replies and private data excluded, rate may be null, and sort behaviors (recent vs top). This goes beyond the schema and 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?

Description is front-loaded with 'Use this when...' and each sentence carries useful information: fields returned, exclusions, alternative tool, sort options, and slug lookup. No fluff; well-structured and appropriately sized.

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?

No output schema exists, but the description lists return fields (rate, review, author_username, like_count, created_at) and explicitly states what is excluded. Coverage is complete for a read-only list tool, addressing data shape, filtering, sorting, and prerequisites.

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 documentation covers slug and sort with descriptions, and limit has constraints. The description adds guidance on how to find the slug, but otherwise mostly repeats schema info. Since schema coverage is 67% (medium), the description provides some added value but doesn't fully compensate for missing limit semantics.

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?

Clearly states the tool reads product reviews on uneed.best, with specific verb 'read', resource 'product reviews', and distinguishes from get_product's aggregates. Mentions testimonials and star ratings, making the 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?

Explicitly contrasts with get_product: use get_product's stats for aggregates, this tool for the full list. Also instructs to find the slug via search_products or get_product, providing clear when-to-use guidance and alternatives.

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 indicate readOnlyHint=true and destructiveHint=false. The description adds significant behavioral context: rankings cover products launched in the period, ordered purely by community vote (no paid placement), weeks are ISO weeks anchored to America/Los_Angeles, and date parameter behavior. No contradictions.

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 with no fluff, front-loading the main purpose. It could be slightly more concise but is well-structured and efficient.

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 3 parameters and no output schema, the description covers key behavioral aspects: ranking criteria, timezone handling, date semantics. Missing details about pagination or default limit behavior, but overall complete enough for an agent to use 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 has 3 parameters with 33% description coverage. The description explains the semantics of period (enum of time ranges), date (gets period containing the date, omit for current), and limit (default 20, implicitly). This adds value beyond the schema, though limit is not explicitly described.

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 returns community-voted rankings for a period, specifying when to use it ('trending, popular or winning on uneed.best'). It distinguishes itself from sibling tools by describing the unique ranking mechanism and community voting 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?

The description explicitly says 'Use this when the user asks what is trending, popular or winning on uneed.best.' It provides clear context for when to use the tool, though it does not mention when not to use it or list alternative 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?

Annotations already indicate readOnlyHint=true and destructiveHint=false. Description adds context by specifying return fields (slug, description, number of listed products), which is useful beyond 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 concise sentences, front-loaded with usage guidance. No extraneous 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?

For a parameterless tool with no output schema, the description fully explains purpose, usage, and return content. It also connects to sibling tools for further actions.

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?

No parameters (schema coverage 100%), so baseline is 4. Description does not need to add parameter info; it is not missing anything.

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?

Clearly states the tool lists product categories and specifies when to use it (to get valid category values before filtering, or when user asks what Uneed covers). Distinguishes from siblings by mentioning it provides slugs/names for other 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?

Explicitly states when to use: 'when you need valid category values before filtering' or when user asks about coverage. Provides alternative usage: feed slug/name to get_recent_launches and list_deals.

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 indicate readOnly and non-destructive. Description adds context about what is returned (name, slug, URL, category) and optional filtering, which is useful beyond 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 concise sentences covering purpose, usage, and additional guidance. No redundancy.

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?

Simple tool (1 optional param) with all necessary information: what it does, when to use, what it returns, and relation to sibling tools.

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% for the single parameter. Description adds that filtering is optional and references list_categories for format, complementing 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?

Clearly states it lists active deals/discounts on digital products, specific to uneed.best. Distinguishes from sibling tools like get_product by noting that each deal is attached to a 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 when the user asks about discounts, promo codes or deals on digital products.' Also provides alternative: use get_product for full product profile.

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 destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: newest listing first, includes asking price and TTM revenue/profit when disclosed, and optionally includes sold items as comparables. It doesn't describe pagination, but that's a minor gap against a strong read-only hint.

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 compact paragraph that front-loads the primary use case and then covers returns, filters, and a pointer to a sibling. Each sentence contributes value; it's slightly longer than minimal but not wasteful. The ordering is logical and agent-friendly.

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 optional parameters, no output schema, and no nested objects, the description adequately explains what is returned (price, revenue, profit, sold comparables) and how to filter. It also tells where to go for full profiles and contact sellers. The only missing detail is the exact response format, but that's not critical without an output schema.

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 80% (4 of 5 parameters already documented). The description adds context like 'filter by category, maximum price or minimum revenue' and clarifies that min_revenue excludes listings with no disclosed revenue (already in schema), but it doesn't add new semantics beyond what the schema provides, especially for the limit parameter. 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 states a specific action: listing products for acquisition on uneed.best. It clearly distinguishes from siblings like get_product (full profile) and list_categories (categories) by explicitly saying 'Lists products their makers put up for acquisition' and later pointing to get_product for full profiles.

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 first sentence gives explicit when-to-use: 'Use this when the user wants to buy a SaaS, micro SaaS or side project, or asks which businesses are for sale.' It also points to the sibling get_product for details, but doesn't explicitly mention exclusions like when to use search or list_deals. Still, the routing is clear enough for an agent.

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 destructiveHint=false. Description adds that it returns `{id, title, url}` documents with same catalog and relevance ordering as search_products, and instructs to pass id to fetch for full profile. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no fluff. Each sentence provides essential information: usage context, comparison with sibling, output format, and follow-up action.

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 search tool with one parameter, the description covers purpose, output format, relationship with fetch and search_products. No output schema exists, but description adequately explains return values.

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 0% so description must compensate. The single parameter 'query' is implied by 'look up digital tools and products' but not explicitly documented. However, context makes it clear, so minor deduction.

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: 'look up digital tools and products on uneed.best' and distinguishes it from search_products by noting the returned format and use case. It also explains how to further use results with fetch.

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 when to use this tool ('deep research run') and when to prefer the sibling search_products ('when you want structured product fields'). Provides clear context for 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?

With annotations already providing readOnlyHint=true, the description adds behavioral details: full-text and typo-tolerant matching, ordering by relevance and popularity (no paid placement), and the meaning of the premium field. This goes beyond annotations, though it could mention rate limits or pagination.

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 focused paragraph, front-loaded with usage guidance and key behaviors. Every sentence adds value without redundancy.

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 result ordering, the premium flag, and suggests using get_product for details. It covers search capabilities and use cases well, though it could mention return fields or pagination. Overall 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?

Schema coverage is 50% (query described, limit not). The description adds context for query (typo-tolerant, full-text over names/descriptions/tags) but does not mention the limit parameter or its behavior. This partially compensates for schema gaps but not fully.

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: searching for digital tools or products by need, keyword, or name. It specifies the directory (uneed.best) and search capabilities (full-text, typo-tolerant). It distinguishes from sibling tools like get_product by mentioning using a result's slug for full 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?

The description explicitly states when to use this tool ('looking for a digital tool or product by need, keyword or name') and provides guidance on interpreting results (e.g., premium: true is a disclosure, not a quality signal). It references get_product for full profiles, but does not explicitly list when not to use or compare to other siblings like search.

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