Get all copy-and-run ChatGPT prompts in one collection (e.g. slug "skincare" or "email-marketing"). Each prompt includes the full fill-in-the-blank template body, its variables, the recommended AI model, and the source page URL on xpay.sh/prompts.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Collection slug from list_collections, e.g. "coffee" or "seo". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: retrieving prompts by collection, by ID, listing collections, and searching across prompts. No ambiguity between them.
All tool names follow a consistent verb_noun pattern using snake_case (get_collection_prompts, get_prompt, list_collections, search_prompts), making them predictable and easy to understand.
With 4 tools, the server is well-scoped for browsing and retrieving prompts from a library. Each tool serves a necessary function without redundancy or excess.
The tool surface covers all essential read operations: listing collections, retrieving individual prompts, getting all prompts in a collection, and searching. For a read-only prompt library, no obvious gaps exist.