pops4-mcp-catalog

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 generation, shipping time, and revenue attribution, but does not disclose whether the tool mutates state, requires authentication, or if there are side effects like creating records. Some behavioral traits are present but not comprehensive.

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

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

The description is somewhat long (3 sentences) and includes marketing fluff like 'AI-native corporate gifting tool for Fortune 500'. While front-loaded with key outputs, it could be more concise and focus on essential 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?

Given the complexity of 6 parameters and no output schema, the description covers essential outputs (curated kit, subtotal, cost, logistics) and constraints (one-hour quote, shipping). It lacks details on return format or error handling but is reasonably complete for an AI 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%, so the schema already documents all parameters. The description adds meaningful context beyond the schema, such as the dollar ranges for brand_tier tiers and the override behavior of budget_per_head. This adds value without being 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 description clearly states the tool builds 'Live event dashboard programs' generating a complete event program from a catalog, listing specific outputs like curated kit, wholesale subtotal, per-attendee cost, and logistics. It distinguishes from sibling tools like get_quote or get_live_quote which are simpler quotation tools.

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 it's for Fortune 500 and semiconductor companies for one-hour quote enterprise events, but does not explicitly contrast with alternatives like get_quote or get_live_quote. The context is clear but lacks explicit when-to-use vs. when-not-to-use 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?

With no annotations, the description provides sufficient behavioral info: lists categories with counts and samples. No side effects are implied, and for a simple read operation the disclosure is 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?

The description is two sentences: one states functionality, one gives usage guidance. No wasted words, front-loaded with purpose.

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

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

Given no parameters and no output schema, the description covers the essential purpose and context. A small gap is lack of mention of pagination or full output shape, but still complete enough for a simple list tool.

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

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

There are no parameters, so the description does not need to add parameter info. Baseline for 0 params is 4, which 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 all available product categories with counts and sample products, differentiating it from siblings like get_product_catalog and search_products. The verb 'list' and resource 'categories' are specific.

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 advises using this tool 'before searching' to understand available categories. This gives clear context but does not list explicit alternatives or when not to use.

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 discloses that the tool returns a quote within one hour and includes virtual proof links, and that shipping takes days. However, it does not cover authentication, rate limits, or any destructive behavior, which is acceptable for a read-like 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 concise (two sentences) but the first sentence 'One-hour quote enterprise events' is grammatically poor and unclear. The second sentence is well-structured. It could be more polished without adding length.

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

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

Given 5 parameters, no required fields, and no output schema, the description covers the basic purpose and return components (line items, totals, proof links). However, it lacks detail on the two input formats and does not mention pricing or error conditions. It is moderately 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%, so baseline is 3. The description mentions 'list of product SKUs and quantities, optional shipping ZIP and event date', which aligns with the parameters. However, it does not explain the two alternative input methods (items vs product_skus+quantities), missing an opportunity to add 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 states the tool returns a wholesale quote with specific components (line items, totals, virtual proof links) for a list of SKUs and quantities, with optional shipping ZIP and event date. The purpose is clear, but the first sentence 'One-hour quote enterprise events' is fragmented and slightly confusing, reducing clarity from a 5.

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?

There is no explicit guidance on when to use this tool versus alternatives like get_quote. The description implies it's for enterprise events and fast turnaround (one-hour) but does not state when not to use it or compare with siblings.

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

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

No annotations provided, so description bears full burden; it mentions returned data types but lacks details on error handling, auth requirements, or side effects (though get is typically safe).

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 that front-loads action and resource, includes key details without waste, earning 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?

Adequately covers what the tool returns for a simple get-detail tool with one parameter and no output schema; minor gap in not addressing error responses or missing slug behavior.

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

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

Schema coverage is 100% with parameter description 'Product slug (from search results)', and the description adds context that slug comes from search results, going 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?

Clearly specifies 'Get full details for a single POPS4 product', listing included fields (description, pricing, care instructions, etc.), and distinguishes from sibling tools like search_products (list) and get_product_catalog.

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?

Implies use when needing full details for a specific product by slug, but no explicit when-to-use, when-not-to-use, or alternative tool guidance despite multiple relevant siblings.

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

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

No annotations are provided, so the description carries the full burden. It discloses the 70,000+ size and default tree behavior, which is positive. However, it does not mention response format, pagination, or any rate limits. The schema has a 'limit' parameter but the description doesn't explain its effect on the response. This is adequate but could be improved.

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, front-loaded with the purpose, then filtering options, then default behavior. Every sentence adds essential information without redundancy. It is highly efficient and easy 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?

Given the tool has 4 optional parameters, no required parameters, and no output schema, the description covers the core functionality and default behavior. It lacks details about the output structure (e.g., what does a filtered result look like?) and pagination (limit param is only in schema). However, it provides enough context for most use cases, so slightly above average.

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 baseline is 3. The description adds value by explaining the default behavior when no filters are applied (returns category tree with counts), which goes beyond the individual parameter descriptions. It also provides context that the catalog is 70,000+ items, implying the need for filtering.

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 verb 'Return' and resource '70,000+ product catalog' for AI-native corporate gifting. It distinguishes from siblings by specifying filtering options and default behavior (category tree with counts), which differentiates it from sibling tools like 'get_categories' 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 provides clear usage context: when to filter by keyword, brand, or category, and the default no-filter case returns a category tree with counts. It does not explicitly mention when not to use or compare to alternatives like 'get_product' for single items, but the context is sufficient for most scenarios.

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 for behavioral disclosure. It explains the tool generates a quote and returns specific fields, including contact info for order finalization. However, it does not mention any side effects, authorization needs, or rate limits.

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 that efficiently convey purpose, input, and output. No filler, but could be slightly more structured (e.g., bullet points).

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 adequately lists return values (line items, totals, contact info). However, it lacks details on potential errors or pagination.

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?

Description adds context beyond the input schema by specifying 'POPS4' products, which may indicate a product line. The schema is fully documented, but the description clarifies the scope (wholesale, POPS4) beyond the array of items.

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

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

Description clearly states the tool generates a wholesale pricing quote for POPS4 products with quantities, and lists the returned elements. However, it does not differentiate from sibling tools like get_live_quote, which may also generate quotes.

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 is provided on when to use this tool vs alternatives (e.g., get_live_quote). There is no mention of prerequisites or contexts where this tool is preferred.

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 for behavioral disclosure. It explains the conditional return behavior (live data vs. model+link) and explicitly states 'No fabricated figures', indicating data integrity. It does not mention permissions or rate limits, but for a read-only tool this is acceptable.

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 three sentences, starting with a noun phrase that could be a header, then clear action. It is efficient with no wasted words, though the first sentence could be more directly integrated.

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

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

For a simple tool with one parameter and no output schema, the description covers the key points: what is returned under two conditions and data authenticity. It does not explain domain terms like 'pipeline-impact data' but is sufficient for the 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 one parameter 'event_id' described as 'Event id'. The description adds context by linking it to 'event id' in the tool's purpose, but does not provide additional meaning beyond the schema. 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 the tool returns revenue attribution data (pipeline-impact or attribution model) for a given event ID, using a specific verb 'return' and resource 'revenue attribution', and implicitly distinguishes from sibling tools like generate_event_program or get_categories which serve different purposes.

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

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

The description provides clear context on when to use this tool: when pipeline-impact data is recorded it is returned, otherwise the attribution model and dashboard link are returned. It does not explicitly state when not to use or name alternatives, but the conditional logic gives implicit 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?

No annotations are provided, so the description bears full responsibility. It lists what the tool returns (vendor capability card, contact, brands, etc.) but does not disclose behavioral traits like rate limits, data freshness, or whether it's a read-only operation. The information is adequate but lacks depth.

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 somewhat verbose with a run-on list of capabilities (e.g., 'live event dashboard, one-hour quote enterprise events, ships in days Fortune 500'). While it front-loads the purpose, it could be more 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 no parameters, no output schema, and no annotations, the description provides reasonable completeness: it explains the output and positions the tool as a first step. However, it lacks details on authentication or 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?

With 0 parameters, the baseline is 4. The description adds value by detailing the contents of the returned data (contact, ASI number, endpoints, brands, product catalog), which goes 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 states it returns the 'Huanggoodman vendor capability card' and includes contact info, ASI number, endpoints, brands, and product catalog. It also explicitly directs to 'call this first to decide whether to shortlist the vendor,' which distinguishes it from siblings like get_product_catalog or get_categories.

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 'Call this first to decide whether to shortlist the vendor,' providing clear usage context as an initial evaluation tool. It does not explicitly mention when not to use or alternatives, but the directive is strong enough.

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

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

No annotations are provided, so the description carries the full burden. It adds value beyond the schema by mentioning custom imprinting availability and the catalog's size and nature (e.g., wholesale, brand identity). However, it does not disclose behavioral traits such as result ordering, pagination behavior, rate limits, or how empty results are handled. The ambiguity between '70,000+ products' and '11,060 live detail pages' is minor but could confuse.

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

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

The description is concise, consisting of two sentences. The first sentence establishes purpose, and the second expands on features and return values. It efficiently uses words without filler. However, it could be slightly more structured (e.g., separating functionality from return values) without adding length.

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 7 parameters, no output schema, and no annotations, the description is reasonably complete but not exhaustive. It covers what the tool does, what catalog it searches, and what it returns (pricing, images, links). Missing aspects include explanation of parameter interdependencies (e.g., query + category), default behavior (e.g., sorting), and return format details. For a search tool with this complexity, more context would be beneficial.

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

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

Schema coverage is 100%, so the baseline is 3. The description does not add significant semantic context beyond the schema's parameter descriptions. It mentions the catalog's size but does not elaborate on how parameters like brand, category, or query interact. No parameter usage examples or nuanced explanations are provided in the description itself.

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 searches a specific catalog (POPS4 wholesale branded identity infrastructure), quantifies its scope (70,000+ products, 11,060 live detail pages, 30+ categories), and explicitly lists what it returns (wholesale pricing, images, virtual proof links). This distinguishes it effectively from sibling tools like get_product (single product detail) and get_product_catalog (full catalog 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 searching products but does not explicitly provide when-to-use or when-not-to-use guidance relative to sibling tools. No exclusions or alternatives are mentioned. While the purpose is clear, the agent lacks direct guidance on selecting this tool over others like get_product or get_categories.

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?

Discloses that it reads live order records, returns a clear status if not found, routes to concierge, and explicitly states 'No fabricated tracking.' With no annotations, this offers sufficient transparency for a simple read tool, though auth and rate limits are omitted.

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 short but has a fragmented first sentence ('Ships in days Fortune 500') that may confuse. It could be restructured for clarity and conciseness.

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 without output schema, the description adequately covers purpose, behavior on existence/non-existence, and a warning against fabricated data. No major gaps 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 coverage is 100%, so description adds limited value beyond the schema. It mentions using 'order_id' to look up a live record, but no additional format or constraints beyond what the schema provides.

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

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

Clearly states it returns real logistics status for an order id, distinguishing it from siblings like search_products or get_quote. The first sentence 'Ships in days Fortune 500' is confusing but the core purpose is well-defined.

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 some context on behavior when order exists vs not, but lacks explicit when-to-use versus alternative tools. No comparison to sibling tools, leaving the agent to infer from purpose.

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