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The Remote StreamNative MCP Server can publish Orca agents as Model Context Protocol (MCP) tools. This lets IDE copilots, external orchestrators, and other MCP-aware agents call your Orca workloads without building a custom integration layer.

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Automatic discovery and refresh

  • Orca Engine registers agents that expose the required root_agent entry point and marks them as shareable through MCP.
  • The managed server regularly discovers eligible agents and publishes them as MCP tools. Default tool names follow the pattern agent_<tenant>_<namespace>_<agent> when you do not supply overrides.
  • Server-Sent Events (SSE) signal catalog changes. When the MCP server reports tools changed, Orca Engine reloads cached metadata so clients immediately see the latest instructions and tool hints.

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Metadata surfaced to MCP clients

  • Each exported tool includes the agent name, description, and supported session mode so callers understand the conversational context before invoking it.
  • When an agent declares structured input or output channels, the Remote MCP Server maps that contract into MCP tool parameters so clients know which fields to supply.
  • Keep prompt instructions concise and results deterministic where possible so downstream tools can parse responses reliably.

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Customize tool expose metadata

Provide descriptive names and summaries to make the MCP tool easier to be used by LLM agents. 
{
  "env": {
    "MCP_TOOL_NAME": "IncidentResponder",
    "MCP_TOOL_DESCRIPTION": "Guides engineers through triaging active StreamNative Cloud incidents."
  }
}
  • Set MCP_TOOL_NAME and MCP_TOOL_DESCRIPTION as environment variables in the agent configuration through CLI deployment flags.
  • When present, the Remote MCP Server uses these values instead of the default derived name and description.
  • Administrators can require both variables before publishing an agent so only well-documented tools appear in Remote StreamNative MCP server.

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Prepare an agent for MCP publication

  1. Follow the Google ADK agents guide or OpenAI agents guide to produce a deployable artifact.
  2. Store model credentials as StreamNative Cloud secrets and bind them with the --secrets flag during deployment.
  3. Add MCP_TOOL_NAME and MCP_TOOL_DESCRIPTION to the agent environment, then redeploy so the discovery cycle can pick up the customized metadata.

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Control discovery with MCP features

  • The Remote MCP Server exports shareable Orca agents by default when your identity can reach the associated tenant and namespace.
  • To skip agent discovery from a client, send the X-MCP-Features header during connection setup and omit agents-as-tools from the list.
  • Provide the feature explicitly—X-MCP-Features: pulsar-admin,agents-as-tools—when you want to mix agent tools with other MCP capabilities while keeping control over optional features.

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Consume the agent from MCP clients

  • MCP clients invoke the tool using standard call_tool requests. The managed server forwards payloads to the corresponding Orca agent and streams responses back.
  • The MCP server enforces StreamNative Cloud permissions, so users only see agents they are authorized to access.
  • Use StreamNative observability dashboards and snctl agents status to monitor traffic routed through MCP.
This pattern lets you share domain-specific assistants—such as troubleshooting copilots or knowledge workers—with any MCP-aware application while keeping configuration centralized in StreamNative Cloud.