SOMA Brewing Company

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

With no annotations, the description carries the full transparency burden. It details what information is retrieved (pricing tiers, keg/draft options, tasting booking) and assumes no side effects. This is adequate for a read-only tool, though it does not mention auth requirements or response format.

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 sentence that efficiently conveys the tool's purpose, target audience, and contents. No filler words; every component serves a purpose. Front-loaded with the core offering.

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 parameters and no output schema, the description adequately covers the tool's returned information. It lists key content areas (pricing, keg options, tasting booking). Slightly lacking in specifying output format, but acceptable for a simple retrieval 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?

Input schema has zero parameters, so schema coverage is 100% trivially. Baseline for 0 params is 4. The description adds no parameter information, which is acceptable since there are none to document.

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 provides B2B pricing tiers, keg/draft options, and how to book a tasting for restaurants, bars, hotels, and events. It uses specific verb 'get' and resource 'wholesale info', distinguishing it from sibling tools like get_pricing_and_delivery which likely covers general pricing.

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 usage for business customers needing wholesale pricing or keg options. It provides clear context but does not explicitly state when not to use it or mention alternatives. However, the target audience and purpose are well-defined, guiding appropriate invocation.

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

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

No annotations are provided, so the description carries the full burden. It mentions 'detailed spec' but does not disclose behavioral traits such as read-only nature, idempotency, or response structure.

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 sentence that is efficient and front-loaded with key information. No superfluous content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is adequate for a simple tool with one enum parameter. However, it lacks information about the return values (no output schema), which would be helpful for a 'detailed spec'.

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 the description merely lists example slugs without additional semantic detail. Baseline of 3 is appropriate as the schema already defines the parameter.

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 detailed spec for a single beer by slug. The verb 'get' and resource 'beer' are specific, and the sibling tool 'list_beers' implies this is for individual items.

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 usage when a single beer's details are needed, but no explicit guidance on when not to use it or alternatives like 'list_beers' for multiple beers.

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

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

No annotations are provided, so the description carries the full burden of disclosure. It clearly lists the types of information returned (taglines, founder, location, contact details, social handles, and the 2026 pivot). For a read-only brand overview, this is transparent enough, though it does not mention rate limits or authentication requirements, which are not critical for this tool.

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

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

The description is a single sentence that is moderately long due to enumerating the specific elements covered. It is front-loaded with 'Brand overview' and conveys the main purpose efficiently. Minor improvement could be splitting into two sentences for readability, but it remains concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there are no parameters, no output schema, and a straightforward purpose, the description comprehensively covers the tool's scope. It includes the unique and potentially important context of the 2026 pivot, which helps the agent understand the relevance of the 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?

There are no parameters, so the baseline score is 4. The description does not need to explain parameter semantics, and it correctly focuses on the output content. The input schema already indicates no parameters, so no additional information is required.

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 provides a brand overview including specific elements like taglines, founder, location, contact details, social handles, and the 2026 pivot. This distinguishes it from sibling tools that focus on individual beers (get_beer), brewing process (get_brewing_process), or other specific aspects.

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 this is the tool for brand-level information, but it does not explicitly mention when to use it versus alternatives such as get_beer or get_faq. Given the number of sibling tools, explicit guidance would improve clarity, but the context is sufficient for basic usage.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool covers production steps, sourcing, and certifications, but does not mention return structure, side effects, or required permissions. Adequate but could be more informative.

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 sentence conveys the entire scope of the tool without waste. Information is densely packed and front-loaded, making it efficient for an agent to parse.

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 information-retrieval tool with no parameters and no output schema, the description adequately covers what the tool provides. It lists the key content areas, though it could benefit from mentioning the form of output (e.g., text, structured data).

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 no parameters and 100% schema description coverage, so the baseline is 4. The description does not need to add parameter semantics, and it correctly omits param-related info.

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

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

The description explicitly lists the six-step production process, ingredient sourcing, and certifications, clearly indicating the resource and scope. It effectively distinguishes from siblings like get_beer or get_brand by focusing on the brewing process.

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?

No guidance on when to use this tool versus alternatives such as get_beer or get_b2b_wholesale. The description provides no context for selection, leaving the agent to infer solely from the tool name.

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

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

No annotations provided, so description carries full burden. It explains both scenarios (with/without variant IDs) and explicitly states that payment happens on Shopify with human control and that it never charges. Could mention configuration prerequisites but overall good.

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 efficient sentences with no redundancy. Front-loaded with the core purpose, then adds specific behavioral details. Every sentence adds value.

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 key aspects: what it does, two modes, safety reassurance. No output schema exists, but return type (a link) is implicit. Could mention format or example link, but adequate for a straightforward tool.

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

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

Schema coverage is 100% with clear descriptions for all three parameters. Description does not add further meaning beyond what the schema provides, so baseline score 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?

Clearly states it builds a direct buy link for an agent to hand to the user. Describes two distinct behaviors based on configuration, making the purpose unambiguous. Distinguishes itself from sibling tools which cover different aspects like beer info, wholesale, etc.

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?

Implied usage: when an agent needs to provide a purchase link. Explicitly says it never charges, but does not offer when-not-to-use guidance or name alternatives like get_b2b_wholesale for bulk orders. Agent must infer from 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?

With no annotations, the description carries full burden but only lists topics. It lacks disclosure of read-only nature, auth requirements, or return format. Assumes safe use but provides no 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?

Single sentence, front-loaded with key phrase 'All customer FAQs'. Efficiently lists topics without extra filler, but could be broken into bullet points for clarity.

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 parameters and no output schema, the description is the sole source of information. It covers content but not format (e.g., list of Q&A pairs) or how to use it for specific queries. Adequate but not rich.

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 exist, so baseline is 4. The description adds meaning by detailing the FAQ content areas, which compensates for the lack of schema.

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

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

The description clearly states that the tool returns all customer FAQs and lists specific topics (SOMA, ABV, etc.), making it distinct from sibling tools that focus on single aspects. However, it does not explicitly state the verb 'get' or 'retrieve', though it's implied.

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?

No guidance on when to use this tool versus specialized siblings like get_pricing_and_delivery or get_brewing_process. The description does not mention that this is a general overview and that specific tools provide more detail.

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

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

With no annotations, the description carries the full burden. It adds value by specifying per-can and per-100ml values, allergens, and dietary flags. However, it omits details like data source freshness, mutation behavior, or any quotas, making it adequate but not rich.

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

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

A single sentence that front-loads the purpose and efficiently lists output fields. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple retrieval tool with one optional parameter, no output schema, and no annotations, the description sufficiently explains the output format (per-can, per-100ml values, specific fields) and use case (health platforms). It is complete.

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

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

Schema coverage is 100% with a single optional slug parameter (enum). The description does not add extra meaning beyond the schema's description of 'omit for full lineup'. Baseline 3 is appropriate.

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

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

The description clearly states it returns machine-readable nutrition data per beer, listing specific fields (calories, carbs, protein, sugar, ABV, allergens, dietary flags) and context (health, fitness, diet-tracking). It distinguishes from siblings like get_beer (general info) and list_beers (listing).

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 usage for health and diet-tracking platforms, but does not explicitly state when to avoid using it or mention alternatives beyond implied sibling differentiation. No exclusions provided.

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

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

With no annotations, the description bears full burden. It transparently reveals the specific data returned (prices, delivery cities with live/next status, flat fee). It does not mention side effects or auth needs, but for a read-only data retrieval tool, the description is adequate. Minor gap: no mention of whether data is static or real-time.

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 concise sentence front-loading key information (prices, cities, fee). Every part is essential; no waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no parameters and no output schema, the description sufficiently conveys the tool's return data. It provides specific examples and city statuses. However, it does not specify the response format (e.g., JSON structure), which might be needed for parsing but is acceptable for a simple 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?

Zero parameters in schema, so baseline is 4. The description implicitly confirms no parameters are needed, and it adds value by listing exact data points. No further parameter explanation required.

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 specific verb 'get' with resource 'pricing_and_delivery' and provides concrete data examples (prices per can, pack, MRP, delivery cities with status, flat delivery fee). It distinguishes itself from siblings like get_b2b_wholesale or get_beer by focusing on retail pricing and delivery.

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 usage for retrieving retail pricing and delivery info but does not explicitly state when to use this tool versus alternatives (e.g., wholesale pricing via get_b2b_wholesale). No exclusion criteria or context for when not to use it are provided.

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

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

No annotations are provided, so the description carries full burden. It describes the output (recipes and pairings) but does not explicitly state that the tool is read-only, safe, or free of side effects. However, the content strongly implies a read operation with no destructive effects.

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 sentence that front-loads the key deliverables (cocktail recipes, ingredients, method, food pairings). Every part is informative and no words are wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (no parameters, no output schema, no annotations), the description adequately covers what the tool returns. It could be slightly more explicit about the scope (e.g., all available recipes), but it is 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?

The input schema has zero parameters, so the description does not need to add parameter-level detail. The description is consistent and sufficient 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 clearly states the tool returns non-alcoholic beer cocktail recipes (with examples) and food pairing guides. It distinguishes itself from sibling tools like get_beer and get_brewing_process by focusing on recipes and pairings rather than beer properties or production.

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?

Usage is implied by the name and description: call this tool when you need recipes or food pairings for non-alcoholic beer. No explicit alternatives or when-not-to-use guidance is provided, but the context of sibling tools makes the distinction clear.

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

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

No annotations are provided, so the description carries the full burden. It mentions free delivery and pause-anytime, which are behavioral traits, but does not disclose auth needs, rate limits, or return format. For a read-only query, this is minimally adequate.

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

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

Two sentences with zero waste. The key information is front-loaded in the first sentence, and the second adds a beneficial detail. Every word 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 the lack of output schema and zero parameters, the description is fairly complete. It covers the main aspects of what the tool returns. It could be slightly improved by mentioning the response format or how to handle errors, but it is sufficient for 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?

There are no parameters, so the schema provides no information. The description adds meaning by listing what the tool returns (pricing, perks, etc.), which is valuable beyond the empty schema.

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

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

The description clearly identifies the tool as retrieving the three monthly beer subscription tiers with pricing, savings percentage, and included perks. It specifies the exact resource and details, distinguishing it from sibling tools like get_pricing_and_delivery or get_b2b_wholesale.

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 the tool is for monthly subscription plans, but it does not explicitly state when to use this tool versus alternatives like get_pricing_and_delivery or get_b2b_wholesale. No exclusions or alternative guidance is provided.

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 fully discloses the tool's behavior: it is read-only, cannot move or spend coins, requires a short-lived token from the user, and returns a specific error message for invalid tokens. This is comprehensive given no annotations are provided.

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 extremely concise, using three sentences to cover purpose, usage instructions, and error handling. It is front-loaded with the core functionality and avoids any fluff, making it easy for an agent to parse quickly.

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?

There is no output schema, but the description specifies the return expectation (balance plus expiring coins summary and error object for invalid tokens). It covers the authentication mechanism, error recovery, and the tool's limitations, making it fully complete for an agent to select and invoke 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 single parameter 'wallet_token' is already documented in the schema, but the description adds crucial context: how to obtain it (user must generate in the app), its expiration (~10 minutes), and what to do if it's invalid. This adds significant value beyond the schema.

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

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

The description clearly states it is a read-only wallet summary for SOMA Coins, including balance and expiring coins. It distinguishes itself from siblings by emphasizing it cannot claim or redeem coins, and that there are no such tools available, making its 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?

It explicitly instructs the agent to ask the user for a wallet token generated in the app, notes the token expires in ~10 minutes, and explains the expected behavior when the token is invalid. It also clarifies it should not be used for claiming or redeeming coins, providing clear usage boundaries.

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

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

No annotations provided, so description fully carries burden. Discloses idempotency ('signing up twice never double-sends') and side effects (CRM storage, welcome email). Does not mention authentication or permission requirements.

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 efficient sentences with zero filler. Delivers purpose, consent requirement, side effects, and idempotency in a clear front-loaded structure.

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 description adequately explains return effects (welcome email, CRM record). Covers behavioral aspects and consent requirement, making the tool self-contained for an agent.

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

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

Schema coverage is 100% with built-in descriptions for all three parameters. Description adds minimal extra meaning beyond repeating 'optional' for city and name. Baseline 3 is appropriate as schema already documents parameters well.

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 verb 'Signs a user up' and specific resource 'SOMA's pre-launch waitlist (the Founding Fifty)'. Differentiates from sibling tools that are about beer products, brands, and billing.

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 'Only call this with the user's explicit consent' and describes the effects (stores contact, sends welcome email). Does not explicitly mention when not to use, but the consent condition implies strict usage boundaries.

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

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

No annotations are provided, but the description thoroughly lists the data fields returned. However, it lacks explicit behavioral traits such as read-only nature or any ordering, which would add transparency.

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, efficient sentence that packs in all relevant details without wordiness. It is front-loaded with purpose and enumeration.

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 provides a comprehensive list of returned data fields, covering style, profile, ABV, pricing, etc. It is complete for a no-parameter 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?

With zero parameters, the baseline is 4. The description adds no parameter information, but there are no parameters to describe, so this score is appropriate.

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

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

The description clearly states it lists the 'full product lineup' and enumerates specific attributes (style, tasting profile, ABV, etc.). It distinguishes from siblings like get_beer by emphasizing it's the complete lineup.

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 use for a broad overview rather than individual beer details, but does not explicitly state when to use versus alternatives or include any when-not guidance.

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