What does glue-mcp do?
It is an MCP server that connects to AWS Glue Data Catalog, allowing AI assistants to query database and table metadata.
@atcol
Basic information
A model context protocol (MCP) server to AWS Glue Data Catalog
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryTool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
glue-mcp is a Model Context Protocol (MCP) server that connects AI assistants to AWS Glue Data Catalog, enabling LLM-driven exploration of databases and tables in your Glue catalog. It is built in Rust and communicates over SSE transport.
After building the binary, set your AWS credentials and start the server with ./glue-mcp, which listens on 127.0.0.1:8000. Configure an MCP host (e.g., mcphost) to connect to http://localhost:8000/sse using the sse transport.
It is an MCP server that connects to AWS Glue Data Catalog, allowing AI assistants to query database and table metadata.
You need to have AWS credentials configured and be able to run a Rust-built binary.
Data resides in the AWS Glue Data Catalog within your AWS account.
It uses SSE transport on port 8000 and authenticates via your configured AWS credentials.

Model Context Protocol Servers

DeepMark helps teachers deliver rapid, consistent marking with meaningful feedback for every student — in a fraction of the time. What once took a week, now takes one free period.

Yuppsy MCP: build and publish real estate websites from your AI assistant. Yuppsy lets your AI assistant create live websites for real estate agents and individual property listings. You don't need a designer or any code
Hindsight: Agent Memory That Learns

B2B competitive intelligence, live sales battlecards, 5-axis ICP prospect fit scoring, and tech stack teardowns over MCP and REST with cited public-web evidence.
Comments