About Spark MCP
Spark MCP lets AI clients search and inspect Spark’s catalog of agents, skills, prompts, bundles, and MCP connectors. It provides asset details and content alongside popularity and category discovery.
Key features
- 01Search assets by query, type, and domain
- 02Retrieve asset details and content
- 03Browse popular assets and categories
- 04Report outcomes from applied content
Use cases
- 01Find a skill for a coding task
- 02Inspect a prompt before using it
- 03Discover connectors by domain
- 04Report whether fetched content worked
MCP support
The MCP server exposes catalog search, asset lookup, content, and outcome-reporting tools, plus resources for asset content and catalog listings. It can run locally or connect through the hosted server in the Cursor plugin.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://spark.entire.vc/mcp/claude mcp add --transport http evc-spark https://spark.entire.vc/mcp/{"mcpServers":{"evc-spark":{"type":"http","url":"https://spark.entire.vc/mcp/"}}}Open in Cursor{"servers":{"evc-spark":{"type":"http","url":"https://spark.entire.vc/mcp/"}}}Open in VS Code{"mcpServers":{"evc-spark":{"type":"http","url":"https://spark.entire.vc/mcp/"}}}{"mcpServers":{"evc-spark":{"serverUrl":"https://spark.entire.vc/mcp/"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
https://spark.entire.vc/mcp/Auth requiredTools
5| Parameter | Type | Required | Description |
|---|---|---|---|
| query | string | Yes | Search query (title, description) |
| type | enum | No | Filter by asset typeagent | skill | prompt | prompt_chain | mcp_connector | bundle |
| domain | string | No | Filter by domain slug (e.g. 'development', 'marketing') |
| limit | number | No | Number of results (1-50, default 10) |
Details
From the project READMESpark MCP
MCP server for Spark — your AI toolbox for real work. Search and discover agents, skills, prompts, bundles, and MCP connectors.
Quick Start
npx evc-spark-mcp
That's it. No API key needed.
Setup
Claude Code
claude mcp add spark -- npx -y evc-spark-mcp
Claude Desktop
Add to claude_desktop_config.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:
git clone https://github.com/entire-vc/evc-spark-mcp.git
mkdir -p ~/.cursor/plugins/local/spark
cp -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:
| Path | Purpose |
|---|---|
.cursor-plugin/plugin.json | Cursor manifest with explicit relative component paths |
mcp.json | Streamable HTTP connection to https://spark.entire.vc/mcp/ |
skills/use-spark/SKILL.md | Search, inspect, fetch, apply, and report outcomes |
server.json | Remote 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
{
"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
| Tool | Description |
|---|---|
search_assets | Search the Spark catalog by query, type, and domain |
get_asset | Get full details of an asset by slug |
get_asset_content | Get raw prompt/skill content (best for prompts and skills) |
list_popular | List most popular assets by download count |
list_categories | List available domains and AI tags for filtering |
get_api_key_info | Check your API key status and daily usage |
report_outcome | Report what happened when you applied fetched content: applied as is, applied with changes, broke, or not applicable |
Resources
| URI Pattern | Description |
|---|---|
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 Variable | Default | Description |
|---|---|---|
SPARK_API_URL | https://spark.entire.vc/api/v1 | Spark API base URL |
SPARK_SITE_URL | https://spark.entire.vc | Spark website URL (for links) |
Development
# Install dependencies
npm install
# Run in development mode
npm run dev
# Build
npm run build
# Test with MCP Inspector
npm run inspect
Part of the Entire VC Toolbox
| Product | What it does | Link |
|---|---|---|
| Local Sync | Vault ↔ AI dev tools sync | repo |
| Team Relay | Self-hosted collaboration server | repo |
| Team Relay Sync | Obsidian plugin for Team Relay | install · repo |
| OpenClaw Skill | AI agent ↔ vault access | repo |
| Spark MCP ← you are here | MCP server for Spark catalog | this repo |
Community
License
MIT — Copyright (c) 2026 Entire VC
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.