ecommerce-prompts-mcp

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

No annotations provided, so the description bears full burden. It discloses the contents of each prompt, but omits details like pagination, error handling, or whether it's a read-only operation. Adequate 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?

Single sentence, front-loaded with action and key details. No extraneous wording. Efficient and well-structured.

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, but the description lists all returned fields (template, variables, model, source URL). Sufficient for a listing tool; lacks mention of pagination or ordering.

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's parameter guidance ('Collection slug from list_collections') repeats the schema description. Baseline of 3 applies; no additional semantics added.

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 gets all prompts in a collection, specifying the exact content (template body, variables, model, source URL). It distinguishes itself from sibling tools like get_prompt (single prompt) and list_collections (listing collections).

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 examples of slugs and implies usage context ('Get all... in one collection'). It does not explicitly state when not to use or alternatives, but the context is clear enough for this simple tool.

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?

Without annotations, the description clearly indicates this is a read operation returning specific data. It does not mention error handling or permissions, but the nature of a GET is transparent enough.

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, efficiently packed with key information (verb, resource, result fields), 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 output schema, the description sufficiently lists return fields. It also references sibling tools for obtaining the id, making retrieval context clear.

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 clear parameter description. The tool description does not add extra semantics beyond what the schema already provides, so baseline 3 applies.

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

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

The description clearly states 'Get one prompt by its id' and enumerates the returned fields (full template body, variables, recommended model, pro tip, source URL), distinguishing it from sibling tools like search_prompts or get_collection_prompts.

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 explicit when-to-use guidance is provided, but the parameter description implies that the id should be obtained from search_prompts or get_collection_prompts, giving indirect context. However, no direct comparison or exclusion criteria are stated.

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 correctly describes a read-only listing operation with no side effects, detailing the returned data. It discloses the library scope (free ecommerce ChatGPT prompts). No contradictions.

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

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

The description is a single sentence that efficiently conveys the purpose, scope, and return fields without any redundant information. It is front-loaded with the main action and detail, 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?

Given no output schema, the description fully explains the return fields. It covers purpose, optional filter, and output structure. For a simple listing tool with one optional parameter, this is complete and leaves no ambiguity.

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 description adds meaning to the 'kind' parameter by explaining its filter options (store-type categories vs. marketing tasks) and the default (omit for all). Since the schema already has an enum and description, the description provides additional context about the categories and tasks, improving usability.

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

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

The description clearly states the tool lists every prompt collection in the free ecommerce ChatGPT prompt library, specifying the 43 store-type categories and 10 marketing-task sets. It also lists the return fields (slug, label, kind, prompt count, page URL), making the purpose unambiguous and distinct from sibling tools like get_collection_prompts.

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 explains the structure of collections (categories vs. tasks) and the optional 'kind' filter. It implies usage for browsing collections before selecting prompts, but does not explicitly contrast with siblings 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?

No annotations provided, so description must describe behavior. It states returns fields but does not mention that the operation is read-only, any side effects, authentication 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?

Three sentences, front-loaded with purpose, no filler. 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 search scope, return fields, and use case. Lacks pagination details beyond limit param, but adequate for a simple search tool with no output schema.

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

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

Schema coverage is 100% with both parameters described. Description adds minimal extra value (keyword example) but does not significantly enhance understanding beyond schema.

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

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

Description clearly states it searches the whole prompt library by keyword, matching multiple fields. It also mentions return fields (collection, source URL). However, it does not explicitly differentiate from siblings like get_collection_prompts.

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?

Suggests use case 'find prompts for a specific store type or marketing job' but does not provide when-not-to-use or mention alternatives (e.g., get_collection_prompts for known collections).

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