MCP Server
Expose any AI Gateway endpoint as an MCP tool. Agents (Claude Desktop, custom MCP clients) call your endpoints via JSON-RPC at POST /api/{uuid}/mcp — with the full guardrail, audit, and routing pipeline applied to every invocation.
The MCP Server is a feature on AI Gateway projects, not a separate project type. Toggle expose_as_mcp_tool=true on any AI Gateway endpoint and the project's /mcp route serves it as a JSON-RPC tool.
JSON-RPC over HTTP, full pipeline
Every MCP tool call goes through PromptGate's standard guardrail + audit + routing pipeline. The bridge is just the wire format.
Expose endpoints as tools
One toggle on the endpoint. Tool name = endpoint slug. Tool description + schema come from the endpoint's metadata.
Standard JSON-RPC methods
initialize, tools/list, tools/call, ping, batch requests, notifications. Full MCP wire-protocol compliance.
Full guardrail pipeline
tools/call delegates to GatewayService: PII filter, secret scanner, prompt-injection guard, schema validation, rate limits, budgets — all enforced.
Scoped auth
Dedicated mcp token scope. Agents that need MCP get a token with only that scope; can't call /v1/chat/completions directly.
Audited as MCP-native
Every tool call lands in gateway_logs with via=mcp_bridge. The audit story is identical to direct API calls.
Endpoint detail surfaces it
Each MCP-exposed endpoint shows tool name + bridge URL + curl example on its detail page. Required scope is documented inline.
Connect Claude Desktop to your endpoints
Drop the MCP server config into Claude Desktop's claude_desktop_config.json. Every endpoint with expose_as_mcp_tool=true shows up in Claude's tool drawer.
- Tool list refreshes when you flip the toggle in PromptGate's UI
- Per-tool guardrails apply on every invocation
- Cost shows up in the same dashboard as your direct API calls
{
"mcpServers": {
"promptgate": {
"url": "https://promptgate.your.co/api/<uuid>/mcp",
"headers": {
"Authorization": "Bearer pg_live_..."
}
}
}
} Use this when…
Use the MCP Server feature when
- You've already built AI Gateway endpoints and want to expose them to agents
- You want one MCP-server URL with N tools instead of N standalone server processes
- You want guardrails / audit / cost on every MCP tool call
- Your agents are MCP-aware (Claude Desktop, custom JSON-RPC clients)
Pick something else when
- You want to aggregate existing MCP servers behind one endpoint → MCP Gateway
- You're shipping an LLM-backed feature without MCP exposure → AI Gateway alone is enough
- You want OpenAI / Anthropic SDK clients → Agent Proxy
Already running an AI Gateway?
Toggle expose_as_mcp_tool on any endpoint. Your AI Gateway project is now also an MCP server. No new project, no new infra.