About
A lightweight adapter that runs as a STDIO MCP server and forwards requests to a Streamable HTTP MCP Server, enabling instant compatibility for clients that only support STDIO while leveraging new HTTP transport.
Capabilities

Overview
The MCP STDIO to Streamable HTTP Adapter bridges the gap between legacy STDIO‑based MCP servers and the newly introduced Streamable HTTP transport. With the specification for a streamable HTTP endpoint finalized in March 2025, most MCP clients still lack native support for this protocol. Developers can therefore build powerful HTTP‑based servers, but end users cannot connect to them without additional tooling. This adapter resolves that mismatch by acting as a lightweight STDIO server that forwards all MCP traffic to the target HTTP endpoint, automatically handling request/response streaming and authentication.
What It Solves
When a team creates an MCP server that exposes resources, tools, or prompts via the HTTP interface, they traditionally need to wait for client libraries (TypeScript, Python, etc.) to catch up before users can interact with it. The adapter eliminates this waiting period: any MCP client that already supports STDIO—such as Claude Desktop, the Inspector, or custom LLM wrappers—can immediately use the HTTP server by pointing to the adapter. This enables rapid iteration, continuous integration, and a smoother deployment pipeline for new MCP services.
Core Functionality
- Transparent Relay: The adapter listens on STDIO, receives MCP messages, and forwards them as HTTP requests to the configured URI. Responses are streamed back over STDIO, preserving the bidirectional nature of MCP communication.
- Authentication Support: An optional bearer token can be supplied via the environment variable, which the adapter injects into the header for every request.
- Multi‑Server Configuration: By assigning distinct values, a single adapter instance can serve multiple HTTP endpoints. This allows developers to expose several SaaS or internal services from a single process.
- Zero‑Code Integration: Clients only need to specify the adapter’s command and environment variables; no changes are required in their existing MCP client code.
Use Cases
- Rapid Prototyping: Build an HTTP‑based MCP service locally, then expose it to Claude Desktop or other clients immediately for testing.
- Continuous Deployment: Deploy the HTTP server in a staging environment and use the adapter to expose it to automated test suites that rely on STDIO MCP clients.
- Multi‑Tenant Platforms: Run a single adapter process that routes requests to different tenant servers, each identified by a unique .
- Hybrid Workflows: Combine legacy STDIO tools with modern HTTP services without refactoring existing client logic.
Integration into AI Workflows
Developers embed the adapter in their environment configuration (e.g., Claude Desktop’s ) or launch it via a wrapper script. Once running, the adapter appears as a new toolset in the AI assistant’s interface—each HTTP endpoint becomes an independent chat or prompt provider. The assistant can then invoke these tools, receive streamed responses, and incorporate them into the conversation seamlessly. This tight coupling allows LLMs to leverage external APIs, databases, or custom inference engines without breaking the conversational flow.
Standout Advantages
- Immediate Compatibility: No client rewrites or SDK updates are needed; the adapter works out of the box with any existing STDIO‑capable MCP client.
- Simplicity and Performance: Written in TypeScript, the adapter is lightweight, making it ideal for local development or edge deployments.
- Extensibility: By hardcoding the URI and publishing a forked repository, teams can distribute ready‑to‑use adapters for their specific HTTP MCP services.
- Security Flexibility: Optional bearer token support keeps sensitive endpoints protected while still enabling easy integration.
In summary, the MCP STDIO to Streamable HTTP Adapter unlocks immediate access to new HTTP‑based MCP servers for all STDIO clients, streamlining development, testing, and deployment of AI tools across diverse environments.
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