Mcp Hydrolix
Let coding agents run Hydrolix queries so you can explore logs, metrics, and analytics tables without leaving Claude Code or Cursor.
Overview
mcp-hydrolix is an MCP server for the Grow phase that lets AI agents query your Hydrolix analytics and time-series datastore.
What is this MCP server?
- MCP server to query Hydrolix from your agent
- PyPI package mcp-hydrolix (v0.2.3) with stdio transport
- Auth via HYDROLIX_HOST plus token or username/password
- Keeps heavy analytics off copy-paste into chat
- Developer Tools adjacency: natural-language to controlled queries
- Package version 0.2.3; PyPI identifier mcp-hydrolix; stdio transport
- Required env: HYDROLIX_HOST
- Auth options: HYDROLIX_TOKEN or username/password pair
Community signal: 9 GitHub stars.
What problem does it solve?
After ship, answering data questions means leaving your agent IDE for Hydrolix consoles and manually rewriting the same queries.
Who is it for?
Indie operators who already use Hydrolix for logs or metrics and want agent-assisted exploration during growth and incident triage.
Skip if: Builders without a Hydrolix deployment, or teams that only need Postgres-level OLTP from the app database.
What do I get? / Deliverables
Once configured, your agent can call Hydrolix through MCP with your host and credentials, speeding analytics loops from your coding workflow.
- Agent-callable Hydrolix query tools over MCP
- Faster analytics iteration from the IDE without a separate SQL-only toolchain
- Credential-scoped access aligned with your Hydrolix service account
Recommended MCP Servers
Journey fit
Hydrolix access matters most in Grow when you investigate usage, funnels, and production telemetry to decide what to improve next. Analytics is where SQL-style interrogation of large event stores belongs—not raw feature coding.
How it compares
Hydrolix-specific MCP data access, not a generic SQL skill or a hosted BI dashboard product.
Common Questions / FAQ
Who is mcp-hydrolix for?
Solo builders and small teams on Hydrolix who use MCP agents to investigate analytics, logs, and time-series data after launch.
When should I use mcp-hydrolix?
Use it in Grow when you iterate on retention, performance, and product decisions that require querying Hydrolix from your agent environment.
How do I add mcp-hydrolix to my agent?
Install mcp-hydrolix from PyPI (stdio), set HYDROLIX_HOST (required), add HYDROLIX_TOKEN or HYDROLIX_USER/HYDROLIX_PASSWORD, and register the MCP server in your client.