Sparkient MCP Server

MCP (Model Context Protocol) server for the Sparkient Decision Intelligence API. Connect your AI agents directly to Sparkient for sub-100ms structured decisions — no REST client code needed.

Quick Start

Cloud Server (Recommended)

The cloud MCP server at mcp.sparkient.ai wraps the Sparkient REST API as MCP tools. You need a Sparkient API key to connect.

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "sparkient": {
      "url": "https://mcp.sparkient.ai/mcp",
      "headers": {
        "Authorization": "Bearer sk-YOUR_API_KEY"
      }
    }
  }
}

Cursor

In Cursor Settings → MCP, add:

{
  "mcpServers": {
    "sparkient": {
      "url": "https://mcp.sparkient.ai/mcp",
      "headers": {
        "Authorization": "Bearer sk-YOUR_API_KEY"
      }
    }
  }
}

VS Code

Create .vscode/mcp.json in your project:

{
  "servers": {
    "sparkient": {
      "type": "http",
      "url": "https://mcp.sparkient.ai/mcp",
      "headers": {
        "Authorization": "Bearer sk-YOUR_API_KEY"
      }
    }
  }
}

Smithery

Install via Smithery:

npx -y @smithery/cli install sparkient --client claude

Local Development

git clone https://github.com/sparkient/sparkient-mcp-server.git
cd sparkient-mcp-server
pip install -e ".[dev]"

# Set your API key and start
export SPARKIENT_API_URL=https://api.sparkient.ai
python -m sparkient_mcp

Related MCP server: APIClaw

Available Tools

Available Resources

Directory Listings

The Sparkient MCP server is listed on the following directories so agents and developers can discover it:

Smithery Configuration

Smithery discovers tools by scanning the live server. The MCP server includes middleware that serves tool metadata to directory scanners that don't follow the full MCP handshake (sending tools/list without initialize).

Key implementation details:

• Stateless HTTP mode (stateless_http=True): Required for Cloud Run where requests route to different instances.

• Scanner middleware (UnknownMethodGuard): Intercepts discovery requests without a session and serves tool metadata directly from the FastMCP instance. Also returns -32601 for non-standard methods like ai.smithery/events/list.

• Auth: Smithery's gateway passes the user's API key via the Authorization header.

Adding to a New Directory

Most MCP directories discover capabilities by connecting to the server and calling tools/list. The server is designed to respond correctly to both:

1. Standard MCP clients — initialize → notifications/initialized → tools/list (returns via SSE)

2. Directory scanners — tools/list directly without initialize (returns via JSON)

Local Edge MCP Server

For sub-10ms decisions with zero network dependency, use the edge MCP server:

pip install sparkient-edge

Claude Desktop config:

{
  "mcpServers": {
    "sparkient-edge": {
      "command": "python",
      "args": ["-m", "edge"]
    }
  }
}

The edge server uses downloaded edge bundles (CEL rules + ONNX models) for local inference. Export a bundle from the Sparkient dashboard or via the export_edge_bundle MCP tool.

See sparkient-edge on PyPI for details.

Environment Variables

Architecture

AI Agent (Claude/Cursor/VS Code/LangChain)
    ↓ Streamable HTTP + API Key
Sparkient MCP Server (this package)
    ↓ httpx (async HTTP)
Sparkient REST API (api.sparkient.ai)
    ↓
Decision Pipeline: CEL Rules → ONNX Classifier → Gemini Escalation

The MCP server is a stateless thin wrapper. Each request is handled independently — no session tracking. Multiple Cloud Run instances serve concurrent requests behind a single URL.