Log inSubmit server
Alkemi MCP logo

Alkemi MCP

by alkemiai

Queries Alkemi-connected data products in natural language through Snowflake, BigQuery, Databricks, and other sources.

InstallRepositoryWebsite
Full README Compare alternatives Embed widget Claim this listing

Install Alkemi MCP

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "alkemiai-alkemi-mcp": {
      "command": "npx",
      "args": [
        "@alkemiai/alkemi-mcp"
      ],
      "env": {
        "BEARER_TOKEN": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx @alkemiai/alkemi-mcp

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Overview

alkemiai/alkemi-mcp MCP server connects an MCP client to Alkemi’s Streamable HTTP endpoint for natural-language data queries. Alkemi stores database metadata, generates queries, and runs them against connected data products such as Snowflake, Google BigQuery, and Databricks. The package runs locally as a STDIO wrapper and authenticates with a bearer token. Reach for it when agents need shared, product-scoped access to connected analytical data.

Use cases

  • Query Snowflake data through an MCP client
  • Ask natural-language questions about BigQuery datasets
  • Scope an agent to a specific Alkemi data product
  • Share consistent database context across team agents

Key features

  • Natural-language querying
  • Snowflake connectivity through Alkemi
  • Google BigQuery connectivity through Alkemi
  • Databricks connectivity through Alkemi
  • Product-level query scoping
  • STDIO wrapper for Streamable HTTP MCP

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 3d ago

Recent health check succeeded.

18/18 checks healthy over the last 79d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 10d ago via OSV.dev · @alkemiai/alkemi-mcp (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars4Stargazers on the source repository.
npm downloads533Package downloads in the last 30 days.
Last commit1y agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity5 viewsConfig copies, upvotes, and views on AllMCPs.

How Alkemi MCP works

What alkemiai/alkemi-mcp MCP server does

alkemiai/alkemi-mcp MCP server exposes Alkemi data products to MCP-compatible clients through a local STDIO process. The connected Alkemi environment can use data from Snowflake, Google BigQuery, Databricks, and other sources. Rather than requiring each agent to carry its own database schema notes and query examples, Alkemi keeps that context with the data product and uses it to generate and execute database queries.

This setup is intended for teams that want multiple MCP clients or agents to query the same governed data context. A product can be selected explicitly when the client should only work with one part of the available Alkemi data.

How it works

The package acts as a STDIO wrapper around Alkemi’s Streamable HTTP MCP endpoint:

https://api.alkemi.cloud/mcp

An MCP client starts the local process and communicates with it over standard input and output. The wrapper sends requests to Alkemi using the configured bearer token. Alkemi supplies the stored database metadata and query guidance, creates an appropriate query, and queries the connected source.

The PRODUCT_ID setting optionally narrows the server to one Alkemi product. Multiple instances can be configured with different product IDs, allowing separate client entries for different datasets. MCP_NAME can provide distinct names when more than one instance is registered in the same client.

Setup and configuration

Create an API key through datalab.alkemi.ai, then provide it as BEARER_TOKEN.

A direct package-based configuration uses npx to launch the server. BEARER_TOKEN is required for the STDIO integration. MCP_NAME is optional unless multiple instances would otherwise share the same client name. PRODUCT_ID is optional and accepts the ID of the product to scope.

For local development, the repository can be installed with npm install, built with npm run build, and run from the generated build/index.js. npm run watch enables automatic rebuilding during development. The repository also provides npm run inspector for MCP debugging through the MCP Inspector.

Tools and capabilities

The material describes these capabilities:

  • Query Alkemi-connected Snowflake data.
  • Query connected Google BigQuery data.
  • Query connected Databricks data.
  • Work with other data sources connected through Alkemi.
  • Use stored metadata and query examples to inform generated queries.
  • Scope an MCP instance to a selected Alkemi product.

Limitations and notes

alkemiai/alkemi-mcp MCP server depends on an Alkemi account and a bearer token, as well as data products already connected to Alkemi. It does not directly document database credentials, client support beyond the shown Claude Desktop setup, or the full list of supported source systems. Query quality depends on the metadata and examples maintained for the relevant data product.

The provided Claude Desktop examples include placeholder paths for locally built files; those paths must be replaced with an actual checkout location when using the repository build instead of the npm package.

Read the full README View source on GitHub

Reviews

No reviews yet — be the first to share how it worked for you.

Frequently Asked Questions about Alkemi MCP

Run it from the npm package with npx using @alkemiai/alkemi-mcp, and configure BEARER_TOKEN in the MCP client environment.

Yes. The README provides Claude Desktop configuration examples using either the npm package or a locally built Node.js entry point.

BEARER_TOKEN is required for the STDIO integration. MCP_NAME and PRODUCT_ID are optional.

Yes. Alkemi data connected to Snowflake, Google BigQuery, Databricks, and other sources can be queried through the server.

Related MCP servers

All in Data Platforms All alternatives
Add a badge to your READMEShow it’s listed — live SVG, Markdown or HTML.

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:
Live Dynamic SVG PreviewAlkemi MCP AllMCPs Directory Badge
Markdown (GitHub README)
[![AllMCPs](https://allmcps.com/api/badge/alkemiai-alkemi-mcp?style=directory)](https://allmcps.com/mcp/alkemiai-alkemi-mcp)
HTML Embed
<a href="https://allmcps.com/mcp/alkemiai-alkemi-mcp"><img src="https://allmcps.com/api/badge/alkemiai-alkemi-mcp?style=directory" alt="Alkemi MCP on AllMCPs" /></a>