Spark - AI Assets Marketplace

io.github.entire-vc由 GitHub 帳號 entire-vc 發布v4.0.4更新於 Oct 24, 2026

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

已驗證Streamable HTTP可網頁執行知識與記憶AI 與機器學習

概覽

AI 產生的概覽

讓助理從 Spark 市集目錄搜尋並取得 AI 代理、技能、提示詞、套件與 MCP 連接器。

可以做什麼
提供依查詢、類型與領域搜尋 Spark 目錄的工具,可依 slug 取得資產完整詳情,並讀取原始提示詞或技能內容。也能依下載量列出熱門資產、列出可用於篩選的領域與 AI 標籤、查看 API 金鑰狀態與每日用量,並回報所取得內容的套用結果。另有兩個資源以 markdown 形式提供資產內容,以及依類型列出資產。
適用情境
適合希望助理從公開市集發掘並取得現成提示詞、技能、代理或 MCP 連接器,而非從頭撰寫的情境。主要用於瀏覽與取得目錄內容,而非管理本機檔案或工作流程。
執行需求
可作為遠端端點透過 streamable HTTP 連線 spark.entire.vc,也可用 Node.js 透過 npx evc-spark-mcp 在本機執行。不需要 API 金鑰;可選的 Spark API 金鑰透過 X-API-Key 標頭提供。可選環境變數 SPARK_API_URL 與 SPARK_SITE_URL 可覆寫 API 與網站位址。需要能連線至 Spark 服務的網路。
安裝前請注意
可選的 X-API-Key 標頭帶有憑證,應視為機密。report_outcome 工具會把所取得內容的套用成效回報給 Spark 服務,請考量這會揭露哪些使用資訊。取得的提示詞、技能與代理設定來自第三方市集,套用或執行前應先審查。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Spark - AI Assets Marketplace,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/io.github.entire-vc%2Fspark/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "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

來源: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).

輸入參數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').

輸入參數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').

輸入參數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').

輸入參數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().

無需輸入參數。

版本歷史

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

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