Model Context Protocol server that enables AI agents to discover, create, and execute complex, multi-step workflows defined in simple YAML files. Allow your AI agents to better organize their tool usage and provide a more structured way to handle complex multi-step tasks.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"workflows-mcp-server": {
"env": {
"MCP_LOG_LEVEL": "info"
},
"args": [
"workflows-mcp-server"
],
"command": "npx"
}
}
}Are you the author?
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Empower your AI agents with a powerful, declarative workflow engine.
This server supports HTTP transport. Be the first to test it — help the community know if it works.
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Store, query, and create YAML workflow playbooks for LLM agents via MCP. STDIO or Streamable HTTP.
Four tools covering the full workflow library lifecycle — discovery, retrieval, and creation for both permanent and temporary workflows:
| Tool | Description |
|---|---|
workflow_list | List all permanent workflows in the index, with optional category and tag filters. |
workflow_get | Retrieve a complete workflow definition by name, with global instructions prepended. |
workflow_create | Write a new permanent workflow YAML to the library. |
workflow_create_temp | Write a temporary one-shot workflow, indexed but excluded from list results. |
workflow_listList permanent workflows from the in-memory index.
includeTools: true to surface the unique server/tool pairs used across each workflow's stepsworkflow_getRetrieve a complete workflow by name, including the global instructions document.
version to get the highest available match; specify a version for an exact lookupglobal_instructions.md content as globalInstructions — apply these when executing the workflow; null when the file is absentworkflow_list{{input.foo}}, {{steps.X.output.Y}}) are returned verbatim — the server never interpolates themworkflow_createWrite a new permanent workflow to the library.
categories/<slugified-category>/<slugified-name>-workflow.yamlname@version already exi