MCP Bundler Service

The MCP Bundler is a lightweight microservice designed to turn any public GitHub repository into a ready‑to‑deploy bundle. It resolves the common pain point of manually fetching, compiling, and packaging code for use with AI assistants that require executable modules. By accepting a repository URL (and an optional commit hash), the service automatically pulls the source, resolves dependencies, and emits a single JavaScript bundle in either ESM () or CommonJS () form. Developers can then hand the bundle directly to an AI assistant or upload it to Google Cloud Storage for downstream consumption.

What It Solves

When building AI‑driven workflows that rely on external code—such as a Claude assistant executing user scripts—the code must be in a deterministic, self‑contained format. Manual bundling is error‑prone and time‑consuming, especially when dealing with large repositories or multiple dependency trees. MCP Bundler eliminates this friction by providing an HTTP API that automates the entire process, from repository checkout to bundle generation. The ability to pin a specific commit guarantees reproducibility, while optional GCP integration removes the need for manual uploads and ensures that bundles are centrally stored and versioned.

Core Features

• GitHub Integration – Accepts any public repository URL, fetching the latest commit by default or a specified hash for reproducibility.
• Dual Output Formats – Generates bundles in ESM () or CommonJS (), accommodating different runtime environments and AI model expectations.
• Commit Pinning – The parameter lets developers lock the bundle to an exact snapshot, preventing accidental regressions.
• GCP Storage (Optional) – With a service account key, the service can upload the resulting archive to a configured bucket, returning metadata such as bucket name and path.
• Health & Documentation Endpoints – confirms uptime, while exposes Swagger UI for interactive exploration of all endpoints.
• Robust Error Handling – Clear HTTP status codes (, , ) and descriptive messages aid debugging in automated pipelines.

Real‑World Use Cases

• AI Assistant Code Execution – A Claude assistant can request a bundle from MCP Bundler, receive the executable code, and run it in a sandboxed environment.
• Continuous Integration Pipelines – CI jobs can bundle test repositories on demand, upload the result to GCP, and trigger downstream deployment steps.
• Versioned Package Distribution – Teams can publish deterministic bundles tied to commit hashes, ensuring downstream services always use the intended code version.
• Serverless Deployments – Bundles uploaded to GCP can be directly referenced by Cloud Run or other serverless functions, simplifying deployment workflows.

Integration Into AI Workflows

An MCP client can invoke the endpoint, optionally providing an . The response will contain either the raw bundled code or a GCP upload descriptor. The AI assistant can then:

1. Fetch the bundle from the returned URL or directly use the string payload.
2. Instantiate modules in its runtime (Node.js, Deno, etc.) using the specified format.
3. Execute user‑supplied logic while maintaining isolation and reproducibility thanks to the pinned commit.

Because the service is stateless and container‑friendly, it can be deployed behind any API gateway or in a serverless environment, fitting seamlessly into modern AI‑centric microservice architectures.