Eagles and Angels Limited AI Gateway

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

Annotations already convey non-read-only, idempotent, non-destructive behavior. The description adds valuable context beyond this: it returns a cart link and explicitly states that checkout and payment happen securely on the store, setting correct expectations about the tool's side effects and boundary.

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, purposeful sentences: the primary behavior, the security/checkout boundary, and the input requirement. No filler or repetition, and the main purpose 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?

With one well-documented parameter, helpful annotations, and an output schema present, the description provides everything needed to invoke the tool correctly. It also clarifies where the tool's responsibility ends (checkout/payment), making the behavior 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?

The schema already covers 100% of the parameter documentation, including details for product, variant, and quantity. The description adds only 'one or more line items,' which does not meaningfully extend what the schema already provides. 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 uses a specific verb ('Build') and resource ('a ready-to-checkout cart'), and clearly states it returns a cart link. This distinguishes it from the catalog/search sibling tools, which are all about browsing or retrieving content, not cart construction.

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 context: pass one or more line items to build a cart, and the shopper completes checkout/payment on the store. It implies the tool is not for processing payments, but it does not explicitly name alternatives or state when not to use it.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds meaningful behavioral context beyond that: it clarifies that popularity is based on actual captured order units rather than views, ratings, or editorial choices. This helps an agent set correct expectations about the output semantics.

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 core definition comes first, followed by explicit user-intent examples. Every phrase earns its place and the description is immediately scannable.

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, has fully documented optional parameters, full annotation coverage, and an output schema. The description adds the only missing semantic detail—what 'best sellers' means operationally—making it complete for selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with both 'days' and 'limit' fully documented including defaults and maximums. The description adds no additional parameter syntax or format nuance; it provides only high-level context about the ranking, 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 identifies a specific resource ('the store's best-selling / most popular products'), a specific ranking criterion ('real units sold from captured orders'), and concrete intents ('best product', 'most popular', 'top seller'). This differentiates it from sibling tools like list_products and popular_content by scope and metric.

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 maps natural language queries to this tool: 'what is your best product', 'most popular', 'top seller', and recommendation requests. It gives strong positive usage context, though it does not explicitly state when to prefer a sibling like related_products or popular_content.

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 the read-only, idempotent, non-destructive nature. The description adds value beyond that by noting that availability is 'as of the latest catalog sync,' which communicates the freshness/staleness behavior of the data. No contradiction with annotations.

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

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

Two sentences with no wasted words. Purpose is front-loaded, then input guidance, then a relevant staleness caveat. 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 a single parameter, full schema coverage, rich annotations, and an output schema, the description covers all necessary invocation context. The only additional behavioral note worth making, the catalog sync freshness, is included.

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 parameter description already states 'Product slug, or keywords to find the product.' The prose description repeats this exact guidance without adding additional semantic detail, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Check'), a concrete resource ('current price and in-stock status'), and the scope ('each of its variants/options'). It clearly differentiates itself from sibling tools like get_product or search_products by focusing on price/stock availability.

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 invocation guidance: pass a product slug or search keywords. It establishes the context for when the tool is appropriate (checking price and stock) but does not explicitly mention alternatives or exclusions, so it stops short of a 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 declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds the useful detail that it returns full text, but does not describe pagination, errors, or any other runtime behavior. This is adequate given the simple read-only 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?

The description is a single well-structured sentence that states the action, the resource, and the origin of the parameter without any 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 read-only tool with a rich annotation set and an output schema, this description is complete. It tells the agent exactly what to retrieve and where the id comes from; nothing critical 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 schema already describes the single `id` parameter fully, so the description adds little beyond reinforcing that the id comes from search results. With 100% schema coverage, the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb ('Retrieve') and resource ('full text of one document'), and explicitly ties the id parameter to search results. It is clear enough to invoke correctly, though it does not explicitly distinguish itself from sibling tools like get_article 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?

The description gives a clear usage context: use this tool with an id returned from `search`. It implies the intended workflow without explicitly naming alternatives or exclusion criteria, so it stops just short of full routing guidance.

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, idempotent, and non-destructive behavior. The description adds the 'full clean text' detail and the slug derivation, but does not disclose potential errors, availability, or output shape beyond what annotations and output schema already imply.

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?

Single sentence with no filler. The core action, target resource, and key identifier detail are all front-loaded and immediately usable.

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?

This is a simple one-parameter retrieval tool with full annotation coverage and an output schema. The description provides all necessary context for an agent to select and 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?

The schema already documents the slug parameter and gives an example. The description adds extra meaning by defining slug as 'the last path segment of an article URL,' which helps an agent construct the correct value without external knowledge.

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 ('Fetch'), a precise resource ('full clean text of one article'), and the exact identifier ('slug'). It clearly distinguishes this from sibling search/list tools by focusing on a single article retrieval by slug.

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 intended use is implied: call this when you already have an article slug and need the article's text. However, it does not explicitly mention when not to use it or point to alternatives such as search_articles or list_recent_articles.

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, idempotent, and non-destructive behavior, so the bar is lower. The description adds meaningful behavioral detail by specifying exactly what is returned: description, price, and each variant/option with price and stock status.

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?

A single, front-loaded sentence that conveys the operation, resource, key field, and payload scope without any filler. Every phrase 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?

With full parameter coverage, an output schema, and strong annotations covering safety, the description is complete for a single-product retrieval tool. No relevant call-time details are 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?

Schema description coverage is 100%, and the slug parameter already has clear documentation including the 'last path segment' detail. The description's mention of slug/handle adds little beyond what the schema provides, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description states a specific verb ('Get full detail') and resource ('one product by its slug'), and enumerates the content. Its singular focus clearly separates it from siblings like list_products and search_products.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description establishes a clear usage context: use when a single product slug is available and full product details are needed. It does not explicitly name alternative tools or exclusions, but the singular retrieval purpose is unambiguous.

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 readOnlyHint, idempotentHint, and destructiveHint already set, the description only needs to add non-safety behavioral context; it does so by noting the per-category product-count aggregation. It does not contradict the 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?

One sentence, front-loaded with the action and resource, then the use case. Every part earns its place and there is 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?

For a zero-parameter list tool with complete annotations and an output schema, the description supplies the essential purpose and the key output trait. Nothing needed to invoke it correctly 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?

There are zero parameters, so the schema has no semantic gaps and the description correctly focuses on the result shape rather than input details. This matches the baseline for a parameterless 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 names a specific verb ('List'), an explicit resource ('product categories/collections in this store'), and the output detail ('product count each'). This separates it from product- or search-level siblings like list_products and search_products.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'so an agent can browse the catalog by category' gives clear, practical context for when to select this tool over product-level alternatives. It does not explicitly name excluded alternatives or when-not-to-use conditions, so it stops short of a 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 carry readOnly, idempotent, and non-destructive traits; the description adds pagination behavior with offset and the returned field list. It does not mention errors or rate limits, but the annotation coverage lowers the 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?

Three short sentences with zero filler; the core purpose, returned content, and pagination guidance are all front-loaded. 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?

Output schema supplies return structure and annotations cover safety, while pagination guidance appears in both the description and schema. The only gap is explicit alternative selection, which is not required for a straightforward catalog-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 covers both parameters at 100%, including defaults and next_offset semantics. The description adds the phrase 'page with offset' but does not provide additional meaning beyond what the schema already documents.

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?

States a specific verb ('List') and resource ('products in this store') and enumerates the returned fields (title, price, sale price, availability, image, URL). The catalog/browse framing separates it from more targeted tools like get_product or search_products, though it does not name them explicitly.

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 context: it returns the catalog for browsing and instructs to page with offset, explicitly routing cart-building to add_to_cart. It does not state when to prefer this over search_products or best_sellers, but the catalog-browsing purpose is 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 establish readOnly, idempotent, and non-destructive behavior. The description adds useful context about recency ordering and publication scope, but does not disclose details like pagination behavior or whether the list is sorted by publish date ascending/descending.

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?

A single, front-loaded sentence with no filler. It states the verb, object, and scope efficiently.

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 one optional parameter, an output schema, and rich annotations, the description is nearly complete. It covers the core action and scope; only explicit alternative guidance 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?

Schema description coverage is 100% for the only parameter (limit), so the schema already explains parameter semantics. The description adds no parameter-level detail, matching the baseline of 3 when the schema carries the load.

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 ('List') and resource ('most recently published articles') plus a scope qualifier ('from this publication'). This clearly distinguishes it from siblings like get_article, search_articles, and popular_content.

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 implies when to use it (when recently published articles are wanted), but it gives no explicit guidance on alternatives or when not to use it. Sibling tools like popular_content and search_articles could overlap, so some exclusion would help.

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 meaningful behavioral context by clarifying that 'popular' is based on actual AI assistant fetches, not generic page views or sales. This helps the agent interpret results correctly.

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: the first defines the tool's output and ranking basis, the second gives practical invocation examples. Every sentence earns its place and the definition 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?

The tool is simple with two optional parameters, full schema coverage, an output schema, and annotations covering safety. The description fully explains what the tool returns and when to use it, so 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?

Schema description coverage is 100%, with both parameters (days and limit) fully documented in the schema. The tool description adds no parameter-specific detail beyond what the schema provides, so the 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 names a specific resource ('articles') and a specific ranking criterion ('ranked by how often AI assistants have actually fetched them'), which clearly distinguishes it from siblings like best_sellers or list_recent_articles. The verb and scope are clear.

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 use cases: 'what is your most popular article', 'top reads', or recommending what to read. It provides clear context for when to invoke the tool, though it does not explicitly exclude alternatives like best_sellers for product popularity.

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 read-only, idempotent, non-destructive behavior, so the description only needs to add context. It does by disclosing the order-history origin and the category fallback, which explains the tool's data source and behavior if co-purchase data is absent.

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, about 25 words total, with the core algorithm and data-source detail front-loaded and the use cases appended. Every sentence adds value and there is no filler.

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?

With two well-documented parameters, a read-only/idempotent annotation set, and an output schema, the description covers the data source, fallback, and common query intents. There is no additional context an agent needs to select and invoke the tool 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 description coverage is 100%; product and limit are already documented with their meanings and default/max values. The description adds no parameter-level detail beyond the schema, so it hits the baseline expectation without needing to compensate.

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: recommending products frequently bought together, with a real-order-history source and same-category fallback. It is specific about the resource and behavior, but it doesn't name or contrast sibling tools such as best_sellers, so differentiation relies on context rather than explicit statements.

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 'Use for ...' sentence explicitly tells an agent when to reach for this tool ('what goes with this', 'customers also bought', cross-sell). It does not mention when not to use it or point to a particular alternative, but the intended invocation 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?

Annotations already mark the tool readOnlyHint and idempotentHint, so the description doesn't need to restate safety. It adds useful behavior beyond annotations by specifying the result fields (id, title, url) and that full text requires a subsequent fetch call. 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 short sentences that front-load the action and result, then add the fetch pointer. Every clause earns its place and there is no filler.

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 low-complexity tool with one parameter, an output schema, and read-only/idempotent annotations, the description covers the essentials: what it searches, what it returns, and the next step. It could be more explicit about routing to the specialized search siblings, but that is a minor gap.

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 only parameter, query, is already fully described in the schema as 'Search query' (100% coverage), so the description adds little new semantic detail. It does connect the query to searching 'this site's content,' but that is mostly redundant.

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 verb 'Search' and resource 'this site's content' make the action and scope clear, and the return shape (id, title, url) tells an agent what kind of results to expect. It does not explicitly contrast with the sibling tools search_articles and search_products, so the differentiation is only by implied breadth.

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 context: use it to search the site's content, and after getting an id, pass it to fetch for full text. It does not name alternative search tools or state when not to use it, but the generic 'site's content' scope offers adequate guidance.

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 read-only, idempotent, and non-destructive behavior; the description adds useful context beyond that by defining search semantics: keyword-based, scoped to 'this publication', and relevance-ranked ('best-matching'). It also reveals the response shape (titles, dates, URLs, summaries), which is useful even though an output schema exists.

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 operation is front-loaded, and the return summary is compact but informative.

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 2-parameter read-only search tool with a full output schema, rich annotations, and clear sibling differentiation, the description covers everything an agent needs for correct invocation. No missing prerequisites or side effects need disclosure.

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 schema already explains both parameters ('query' and 'limit'). The description confirms keywords in prose but adds no new meaning for limit or formatting, so it stays at the baseline for complete schema 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?

States a specific verb ('Search'), a specific resource ('this publication's articles'), and a search method ('by keyword'), and enumerates the returned fields. In a group with sibling tools like search_products and get_article, this distinguishes itself as the article keyword-search tool.

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 makes the intended use context clear: find articles matching keywords and get brief metadata. It does not explicitly name alternatives or exclusion conditions, but the target resource ('articles') and return fields make it obvious when not to use product-search or single-article 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?

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so no safety contradictions exist. The description adds valuable behavioral context by enumerating exactly what fields are returned (including the conditional 'was' price for sale items) and explicitly stating that filters can be used without a keyword. This goes beyond the annotations and helps the agent understand the tool's output and flexibility, though it does not describe pagination quirks beyond what the schema's offset field already covers.

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, with the core purpose and capability front-loaded in the first sentence. The second sentence expands on filter behavior with concrete examples, and every phrase earns its place—no filler or repetition. It is appropriately sized for the tool's complexity and maintains a clean structure that is 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 covers the essential usage patterns (keyword and/or filters), return fields, and the ability to combine filters without a keyword. The tool has 8 parameters, all optional, and the description provides illustrative examples that help an agent understand how to combine them. Since an output schema is present, the lack of detailed return-value documentation is acceptable. The description is complete enough for an agent to correctly invoke the tool in common scenarios, though it could briefly mention the default limit or pagination (already in schema), so it earns a 4 rather than 5.

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%, with every parameter individually documented in the schema. The tool description adds extra semantic value by explaining that filters can be combined and used without a keyword, which is not fully captured in the schema's per-parameter notes. This enriches the meaning beyond the raw schema, particularly for the interplay of filters. Given the high schema coverage, this extra guidance justifies a score above the baseline of 3.

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 begins with a specific verb ('Search') and a clear resource ('this store's products'), and specifies the ability to search by keyword and/or filters. It distinguishes itself from siblings like search_articles and list_products by explicitly stating it searches products and lists the return fields (titles, prices, availability, image, URLs). No ambiguity remains about what this tool accomplishes.

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 concrete examples of when to use filters without a keyword (e.g., on_sale=true alone, price_max with in_stock_only=true), guiding the agent on valid usage patterns. It does not explicitly contrast with sibling tools like list_products, but the presence of filter combinations and the keyword-optional behavior clearly implies this is the go-to search tool when filters are involved. The guidance is helpful and actionable, but stops short of stating when not to use it in favor of 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 already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral context by revealing that the tool returns links to policy pages and checkout basics, which goes beyond the annotations without contradicting them.

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 brief and front-loaded, immediately stating what the tool provides, then giving concrete usage examples. Every sentence earns its place with no redundant filler.

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 zero-parameter, read-only information tool with an output schema and clear annotations, the description is complete. It states the content scope, the specific links covered, and representative use cases, leaving no gap an agent needs to resolve elsewhere.

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 tool has zero parameters, so the schema requires no additional explanation. The description's examples clarify the kind of natural-language query the tool is meant to serve, which is helpful context even though no parameter semantics are needed.

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 this as the store policy and basics tool, naming checkout location and links to shipping, returns/refund, terms, and privacy pages. The 'Use for' examples make its scope unmistakable and distinguish it from the product/article/search 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 lists representative queries like 'do you ship to X', 'what is your return policy', and 'how do I pay', giving clear context for when to use this tool. It does not explicitly state when not to use it or name alternatives, but the examples are specific enough to route an agent correctly.

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