Deploy custom MCP servers

Blaxel provides a serverless infrastructure to instantly deploy MCP servers. You receive a global inference endpoint for each deployment, and your workloads are served optimally to dramatically accelerate cold-start and latency. The main way to deploy an MCP server on Blaxel is by using Blaxel CLI.

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Deploy an MCP server with Blaxel CLI

This section assumes you have developed the MCP server locally, as explained in this documentation, and are ready to deploy it.

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Serve locally

You can serve the MCP server locally in order to make the main function in server.ts / server.py available on a local endpoint.

Run the following command to serve the MCP server:

bl serve

You can then create an MCP Client to communicate with your server. When testing locally, communication happens over stdio, but when deployed on Blaxel, your server will use WebSockets instead.

Add the flag --hotreload to get live changes.

bl serve --hotreload

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Deploy on production

You can deploy the MCP server in order to make it available on a global hosted endpoint. When deploying to Blaxel, you get a dedicated endpoint that enforces your deployment policies.

Run the following command to build and deploy the MCP server on Blaxel:

bl deploy

You can now connect to the MCP server either from an agent on Blaxel (using the Blaxel SDK), or from an external client that supports WebSockets transport.

import { blTools } from "@blaxel/sdk";

const tools = blTools(['blaxel-search']).ToVercelAI();
// or
const tools = blTools(['blaxel-search']).ToLangChain();
// or
const tools = blTools(['blaxel-search']).ToLlamaIndex();
// or

// …

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Overview of deployment life-cycle

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Manage revisions

As you iterate on your software development, you will need to update the version of a function that is currently deployed and used by your consumers. Every time you build a new version of your function, this creates a revision. Blaxel stores the 10 latest revisions for each object.

Revisions are atomic builds of your deployment that can be either deployed (accessible via the inference endpoint) or not. This system enables you to:

• rollback a deployment to its exact state from an earlier date
• create a revision without immediate deployment to prepare for a future release
• implement progressive rollout strategies, such as canary deployments

Important: Revisions are not the same as versions. You cannot use revisions to return to a previous configuration and branch off from it. For version control, use your preferred system (such as GitHub) alongside Blaxel.

Deployment revisions are updated following a blue-green paradigm. The Global Inference Network will wait for the new revision to be completely up and ready before routing requests to the new deployment. You can also set up a canary deployment to split traffic between two revisions (maximum of two).