snowflakebigquerydatabricksdata-platformsmcp
Queries Alkemi-connected data products in natural language through Snowflake, BigQuery, Databricks, and other sources.
Quick Install
Automated & IDE SetupCopy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
We couldn’t automatically confirm this listing starts correctly
We ran the install command below 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-mcpNo 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 14d ago.
Manual Client & Custom JSON ConfigExpand JSON â–¾
Client Config & Setup
Configure Environment Variables (API Keys, Tokens, Options):
Add required secrets below — values are included directly in the generated snippet so you can copy and paste with confidence.
Quick Add:
Choose your client or environment
Target File:
💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Overview
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
Capabilities & Tool Schemas
Inspect callable tools, capabilities, and parameters exposed to AI agents by Alkemi MCP.
Extracted Tool Capabilities
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
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. The README shows installation through the npm package @alkemiai/alkemi-mcp and configuration for Claude Desktop.
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.
The exact individual MCP tool names are not listed in the provided material, so this description does not infer them.
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.
Related MCP Servers
Adoption & maintenance
Factual signals from GitHub, npm, and our automated checks — not a rating.
GitHub stars
4
Stargazers on the source repository.
Last commit
11mo ago
Most recent push to the default branch.
Install check
Inconclusive
Didn't respond in our test window — often a slow first install.
Directory activity
4 views
Config copies, upvotes, and views on AllMCPs.
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Frequently Asked Questions about Alkemi MCP
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CategoryData Platforms
PricingFreemium
14/14 checks healthy over the last 56d
40Quality signal: Fair · 40/100How this signal is calculated ▾
Server availabilityNot measured
Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.
Verified ownership10/20
Documentation & tools18/30
Adoption & activity1/15
Community engagement1/10
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Scanned 2d ago via OSV.dev · @alkemiai/alkemi-mcp (npm)
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