Sparkient MCP Server

Sub-100ms Decision Intelligence API. Instant structured decisions for content moderation, fraud detection, ticket triage, and approval workflows.

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

The cloud MCP server atmcp.sparkient.aiwraps the Sparkient REST API as MCP tools. You need aSparkient API keyto connect.

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

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

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

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

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

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

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 (sendingtools/listwithoutinitialize).

- 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-32601for non-standard methods likeai.smithery/events/list.
- Auth: Smithery's gateway passes the user's API key via theAuthorizationheader.

Most MCP directories discover capabilities by connecting to the server and callingtools/list. The server is designed to respond correctly to both:
- Standard MCP clients—initialize→notifications/initialized→tools/list(returns via SSE)
- Directory scanners—tools/listdirectly withoutinitialize(returns via JSON)

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

{ "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 theexport_edge_bundleMCP tool.

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.

Zero-setup MCP gateway securely connecting AI to your tools with authentication and workflows

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