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Build, validate, and run visual automation workflows through natural language.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "qontinui-lib-mcp" yet — see the docs or source repo.
Create a visual workflow that fetches new data from a specified API every hour, removes duplicates, writes the results to a database, and sends an alert on failure. Generate the workflow structure directly.
An executable workflow definition with nodes, connections, triggers, and error-handling logic.
I want a workflow that collects form submissions, uses an LLM to summarize the content, and then syncs the result to Slack. Search for and recommend the best node combination.
A list of matching nodes with their purposes and a recommended connection order.
Check whether this automation workflow has missing parameters, invalid connections, or potential runtime errors; if it passes validation, run it immediately and return a summary of the results.
A validation report, and if valid, execution status, key logs, and a result summary.
Safely query and manage qTest data for test automation and collaboration.
Run local QA checks for APIs, test cases, errors, and SLA evaluation.
Search the web, extract content, research papers, and consult library docs.
Rank and shortlist MCP tools for natural-language queries with zero dependencies.
Give AI coding agents persistent semantic memory and workspace-aware code search.
Connect to Qdrant for semantic search and document relationship analysis.