Spark - AI Assets Marketplace

io.github.entire-vc由 GitHub 账号 entire-vc 发布v4.0.4更新于 Oct 24, 2026

Search and fetch AI agents, skills, prompts and MCP connectors from the Spark marketplace.

已验证Streamable HTTP可网页运行知识与记忆AI 与机器学习

概览

AI 生成的概览

让助手从 Spark 市场目录中搜索并获取 AI 智能体、技能、提示词、工具包和 MCP 连接器。

可以做什么
提供按查询、类型和领域搜索 Spark 目录的工具,可按 slug 获取资产完整详情,并读取原始提示词或技能内容。还可按下载量列出热门资产、列出可用于筛选的领域和 AI 标签、查看 API 密钥状态与每日用量,并上报所获取内容的应用结果。另有两个资源以 markdown 形式提供资产内容,以及按类型列出资产。
适用场景
适合希望助手从公开市场发现并获取现成提示词、技能、智能体或 MCP 连接器,而不是从零编写的场景。主要用于浏览和获取目录内容,而非管理本地文件或工作流。
运行要求
可作为远程端点通过 streamable HTTP 连接 spark.entire.vc,也可用 Node.js 通过 npx evc-spark-mcp 在本地运行。无需 API 密钥;可选 Spark API 密钥通过 X-API-Key 请求头提供。可选环境变量 SPARK_API_URL 和 SPARK_SITE_URL 可覆盖 API 与站点地址。需要能访问 Spark 服务的网络。
安装前请注意
可选的 X-API-Key 请求头携带凭据,应视为机密。report_outcome 工具会把所获取内容的应用效果反馈回 Spark 服务,请考虑这会暴露哪些使用信息。获取的提示词、技能和智能体配置来自第三方市场,应用或执行前应先审查。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Spark - AI Assets Marketplace,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.entire-vc%2Fspark/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "spark-ai-assets-marketplace": {
      "type": "http",
      "url": "https://spark.entire.vc/mcp/"
    }
  }
}

README

Spark MCP

[npm version] [License: MIT] [MCP] [Install via Spark] [Smithery]

MCP server for Spark — your AI toolbox for real work. Search and discover agents, skills, prompts, bundles, and MCP connectors.


Quick Start

bash
npx evc-spark-mcp

That's it. No API key needed.


Setup

Claude Code

bash
claude mcp add spark -- npx -y evc-spark-mcp

Claude Desktop

Add to claude_desktop_config.json:

json
{  "mcpServers": {    "spark": {      "command": "npx",      "args": ["-y", "evc-spark-mcp"]    }  }}

Install in Cursor

This repository includes a Cursor plugin with a remote Spark MCP server and the use-spark skill. No Node.js process or API key is needed for anonymous access.

To test a local install:

bash
git clone https://github.com/entire-vc/evc-spark-mcp.gitmkdir -p ~/.cursor/plugins/local/sparkcp -R evc-spark-mcp/.cursor-plugin evc-spark-mcp/mcp.json \  evc-spark-mcp/server.json evc-spark-mcp/skills ~/.cursor/plugins/local/spark/

Restart Cursor or run Developer: Reload Window, then open Customize and confirm the use-spark skill and Spark MCP server. Enable the server if prompted; its tools include search_assets, get_asset, get_asset_content, and report_outcome. Try: “Find a Spark skill for reviewing Python code.” Local plugin imports must be allowed by your team administrator. Copy the files: Cursor skips symlinks whose target is outside ~/.cursor/plugins/local. See Cursor's local plugin instructions.

For optional authenticated access, set SPARK_API_KEY in the environment used to launch Cursor and restart it. mcp.json uses Cursor's ${env:SPARK_API_KEY} interpolation for X-API-Key; leave it unset or empty for anonymous access. Never put a key in the repository. Get a key from your Spark profile.

Plugin contents:

PathPurpose
.cursor-plugin/plugin.jsonCursor manifest with explicit relative component paths
mcp.jsonStreamable HTTP connection to https://spark.entire.vc/mcp/
skills/use-spark/SKILL.mdSearch, inspect, fetch, apply, and report outcomes
server.jsonRemote server metadata; its version matches the plugin version

The plugin follows the hosted server version (4.0.4), independently of the npm stdio package version. When updating the hosted version, update server.json and the plugin manifest together; npm test checks that they match.

OpenClaw

json
{  "mcpServers": {    "spark": {      "command": "npx",      "args": ["-y", "evc-spark-mcp"]    }  }}

ChatGPT (via MCP bridge)

Any MCP-compatible client works — just point it at npx evc-spark-mcp.


Tools

ToolDescription
search_assetsSearch the Spark catalog by query, type, and domain
get_assetGet full details of an asset by slug
get_asset_contentGet raw prompt/skill content (best for prompts and skills)
list_popularList most popular assets by download count
list_categoriesList available domains and AI tags for filtering
get_api_key_infoCheck your API key status and daily usage
report_outcomeReport what happened when you applied fetched content: applied as is, applied with changes, broke, or not applicable

Resources

URI PatternDescription
spark://assets/{slug}Asset content as markdown
spark://catalog/{type}Asset list by type (agent, skill, prompt, etc.)

What's in the Catalog

Spark is a marketplace of AI workflow assets:

  • Agents — ready-to-use AI agent configurations
  • Skills — capabilities you can add to your agent (like this MCP server)
  • Prompts — tested prompt templates for specific tasks
  • Bundles — curated sets of tools that work together
  • MCP Connectors — integrations with external services

Find, share, and monetize AI tools — all in one place.


Configuration

Environment VariableDefaultDescription
SPARK_API_URLhttps://spark.entire.vc/api/v1Spark API base URL
SPARK_SITE_URLhttps://spark.entire.vcSpark website URL (for links)

Development

bash
# Install dependenciesnpm install
# Run in development modenpm run dev
# Buildnpm run build
# Test with MCP Inspectornpm run inspect

Part of the Entire VC Toolbox

ProductWhat it doesLink
Local SyncVault ↔ AI dev tools syncrepo
Team RelaySelf-hosted collaboration serverrepo
Team Relay SyncObsidian plugin for Team Relayinstall · repo
OpenClaw SkillAI agent ↔ vault accessrepo
Spark MCP ← you are hereMCP server for Spark catalogthis repo
[evc-spark-mcp MCP server]

Community

Find us in other directories

[MCP Badge] [Glama] [Smithery]

License

MIT — Copyright (c) 2026 Entire VC

来源:README.md,提交 572772b

工具

6

search_assets

Search assets

Search the Spark AI assets marketplace for agents, skills, prompts, prompt chains, MCP connectors and bundles. For when a text query is available but the slug is not known; get_asset describes a known slug. Returns numbered summaries with type, downloads, outcomes, price, tags and URL, or a no-results message. No login required; no acquisition is made. Unmatched nonempty queries may be recorded as unmet catalog needs. A nonempty job ranks text matches by task fit, even with a non-combo sort. For example, search_assets(query='rate limiting', job='add a rate limiter', limit=5).

输入参数jobsorttypelimitquery必填domain

get_asset

Get asset details

Get full details of a Spark asset by its slug — description, metadata, outcome reports, bundled files and the URL on spark.entire.vc. For when a known slug needs inspection before acquisition. Returns Markdown details, which may include embedded content; get_asset_content obtains the raw content and a receipt. Public catalog lookup, no login or download recorded. Missing or unavailable slugs return a not-found message. For example, get_asset(slug='vb-seo-expert').

输入参数slug必填

get_asset_content

Get asset content

Get the raw content of a Spark asset (prompt text, skill instructions, agent config) — the actual text to apply, not just a description of it. For when a known asset's content is needed for application; get_asset provides details without acquisition. Each successful fetch records a download/acquisition; repeated calls create new receipts and content may change after updates. Free content needs no login. Paid content requires a Spark API key (X-API-Key) or Bearer token and prior purchase or ownership; this tool makes no purchase. Returns content and an application_id for report_outcome, or a not-found/access message. Content may be a source link rather than embedded text. For example, get_asset_content(slug='vb-seo-expert').

输入参数slug必填

report_outcome

Report outcome

Records the result of applying a fetched Spark asset: applied as is, changed, broke, or not applicable. For when the outcome is known and an application_id from get_asset_content is available, rather than a star rating. Mismatched user/session receipts are rejected; asset owners cannot report on their own assets. Submitting the same application_id updates the report. Authenticated reports count, but team reports do not affect rankings; anonymous reports are unverified. Returns a saved/updated confirmation, or a validation/receipt error. Fields `task`, `note`, `changed_what`, `failed_at`, `expected`, `got` are published on the asset's public page on spark.entire.vc, visible to any visitor. Do not include client data, private paths, keys, emails or URLs with tokens. Your identity is never shown on that page. Example: report_outcome(application_id='<receipt>', result='applied_as_is', task='add a rate limiter').

输入参数gotnotetask必填modelresult必填expectedfailed_atchanged_whatapplication_id必填

list_categories

List categories

List available categories (domains and AI tags) in the Spark marketplace. For when domain filter values or taxonomy are needed; search_assets returns assets rather than categories. Takes no parameters. Returns Markdown lists of AI tag names/slugs and domain names/slugs grouped by parent category; domain slugs are accepted by search_assets' domain filter. AI tags are not a search_assets filter. Public catalog lookup, no login or download recorded. For example, list_categories().

无需输入参数。

版本历史

2
  1. v4.0.4最新Oct 6, 2026
  2. v4.0.3Sep 23, 2026

评分

已满足适用检查项的 100%

该评分衡量此收录条目的完整程度,依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价,也不表示服务器安全或当前可用:可连通性是带日期的检查结果,评分也不包含安装量或用户评价。

  • 近 30 天内可连通必需25 分
  • 已声明认证方式必需10 分
  • 已列出工具必需15 分
  • 描述必需15 分
  • 图标10 分
  • 文档15 分
  • 许可证10 分