promptbase-marketplace
Server Details
Search published AI prompt products on PromptBase.art: titles, prices, ratings, URLs.
- Status
- Healthy
- Uptime
- 98.9% over 22 days
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
Scored across 2 tools
The two tools form a clear list/detail pair: list_prompts returns multiple summarized products, while get_prompt returns a single full product by slug. There is no meaningful overlap or ambiguity between them.
Both tool names follow the verb_noun pattern with list_prompts and get_prompt. The plural list form and singular get form are conventional and predictable.
Two tools is at the thin end of what feels like a complete surface, though both tools are essential to the browse-and-detail workflow. It is borderline but not excessive.
The pair covers the main read-only marketplace workflow: search/list prompts and retrieve full details. Minor gaps such as explicit pagination or structured category/model filters prevent a perfect score.
Available Tools
2 toolsget_promptAInspect
Get full details of a single published prompt product by its slug (the URL path after /marketplace/).
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The prompt slug, e.g. watercolor-storybook-illustrations |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden. 'Get' implies a read operation and 'published' scopes the resource, but it does not disclose not-found behavior, authorization requirements, or response format. This is adequate but not rich for a tool with zero annotation coverage.
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 entire description is one focused sentence that states the action, resource, and parameter format without repetition or filler. It is front-loaded with the core purpose and wastes no 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 one-parameter read operation with no nested objects or output schema, the description is largely complete: it identifies the input, the resource, and the outcome. 'Full details' is somewhat general, but nothing invocation-critical is missing for an agent deciding whether to call this 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?
The schema already documents the slug parameter with an example, so the baseline is 3. The description adds extra semantic value by explaining that the slug is the URL path after '/marketplace/', which clarifies the expected format beyond the schema example.
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 ('Get'), a clear resource ('full details of a single published prompt product'), and the exact lookup key ('by its slug'). This distinguishes it from the sibling list_prompts by singular-object scope and retrieval mechanism.
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 it clear to use this tool when you have a specific slug and want the full details of one prompt. It does not explicitly name list_prompts as the alternative for browsing/listing, but the singular 'single prompt product' and sibling name imply the distinction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_promptsAInspect
List published AI prompt products on PromptBase.art marketplace. Returns slug, title, price, category, model, rating, salesCount, short description and the /marketplace/ URL. Search/filter by optional query.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Optional keyword to filter by title/description | |
| limit | No | Max results (1-50, default 20) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full responsibility for behavioral disclosure. It does state what the tool returns and that it filters by an optional query, but it does not mention ordering, pagination behavior, whether unpublished items are ever included, or any access constraints. It is 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?
The description is two sentences with no filler. It front-loads the core action, then provides return-value detail, and closes with filter behavior. 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 no output schema, enumerating the returned fields is valuable, and the description covers the essential behavior for a simple list tool with two optional parameters. It is slightly incomplete regarding pagination and ordering, but not to a degree that would prevent correct 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?
The input schema already documents both parameters with 100% coverage: q filters by title/description and limit sets max results. The description only echoes this with 'Search/filter by optional query,' adding no new meaning beyond the schema, so the baseline of 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 opens with a specific verb and resource: 'List published AI prompt products on PromptBase.art marketplace.' It also lists the exact return fields and URL pattern, and clearly differentiates from the sibling get_prompt by indicating this is a multi-result listing/search operation rather than a single-item fetch.
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 case is clear: search or browse the marketplace with an optional query. It does not explicitly name get_prompt as the alternative for retrieving a single prompt, but the listing vs. retrieval distinction is strongly implied by the description and the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
- First observed
get_prompt - First observed
list_prompts
Related MCP Connectors
AI shopping search: real, in-stock US and Amazon products with prices, ratings and buy links.
The Wikipedia of AI prompts: search 900+ curated prompts by model, style and type, in 7 languages
AI marketplace: search, buy, sell across Amazon, eBay, AliExpress. 13 tools.
Search, preview, and buy 500+ MIT-licensed digital developer products from Datanest Stores.
Related MCP Servers
FlicenseBqualityDmaintenanceEnables searching for high-quality AI art prompts from Banana Prompts by tags or keyword queries, with direct links to full prompts and images.240 npm4-- AlicenseAqualityDmaintenanceCommunity-driven library of tested prompts for AI agents, enabling search, retrieval, sharing, and rating of prompts.5MIT
- FlicenseNot gradedqualityBmaintenanceEnables AI agents to search and compare live UK product prices from marketplaces like eBay and Amazon, returning normalised JSON with direct buy links.-
- AlicenseNot gradedqualityDmaintenanceEnables product search and retrieval from e-commerce APIs, returning markdown-formatted product listings with clickable links and prices for easy shopping assistance.2 npmMIT
Glama MCP Gateway
Add one secure layer between your agents and this server.