MCP Server

What the Agentled/mcp-server MCP server does

The Agentled/mcp-server MCP server gives MCP-compatible clients access to Agentled workspaces and their workflow runtime. It supports creating, inspecting, editing, validating, publishing, starting, and monitoring workflows. Workflow definitions can include triggers, AI actions, AI actions with built-in tools, app actions, JavaScript code, Knowledge Graph synchronization, approvals, sharing, waits, branches, parallel paths, loops, returns, milestones, and agent orchestration.

The server also exposes workspace memory and Knowledge Graph operations. Agents can inspect memories, knowledge lists, and knowledge text before placing workspace-specific strategy or criteria into prompts. Stored information can then be read at runtime by workflow steps, keeping strategy data separate from workflow wiring.

How it works

A typical authoring sequence is create_workflow with only a name and goal, followed by add_step calls, validate_workflow, and publish_workflow. Adding one step at a time returns validation feedback early and helps reveal variable bindings such as {{input.X}} and {{steps.Y.Z}}. Existing workflows can be changed with focused tools such as update_step, add_step, remove_step, and update_workflow_context.

For live workflows, configuration changes are routed to a draft snapshot. Agents can inspect that draft, promote it to live, or discard it. Snapshots can also be created, listed, inspected, restored, and deleted. Workflows can be exported as portable JSON and imported into another workspace. The server also supports previewing and importing n8n JSON, with the import entering a draft review state rather than being applied automatically.

Execution is asynchronous. start_workflow accepts input data and may return an execution ID or an input ID that must be matched through list_executions. get_execution returns completed step results, while timeline tools can help inspect a particular invocation. Mock-enabled steps use mock data by default; useMocks: false forces a real run.

Setup and configuration

The Agentled/mcp-server MCP server is distributed as the npm package @agentled/mcp-server. A local stdio installation can be registered with:

claude mcp add --transport stdio --scope user agentled \
  -e AGENTLED_API_KEY=wsk_... \
  -- npx -y @agentled/mcp-server

The API key is generated in Agentled under Workspace Settings > Developer and starts with wsk_. The repository also documents a Claude Code plugin that installs the server and an Agentled skill together, but the plugin and direct MCP registration should not both be used for the same setup. Local development can point a built entrypoint at an AGENTLED_URL, such as a local application instance.

Tools and capabilities

The Agentled/mcp-server MCP server includes tools for:

• Listing and reading workflows, steps, executions, timelines, credits, drafts, and snapshots.
• Creating, updating, moving, removing, validating, publishing, pausing, archiving, exporting, importing, and deleting workflows.
• Reading step schemas and app action schemas before authoring pipeline definitions.
• Running AI models, built-in tools, app integrations, browser automation, scraping, communications, media generation, and Knowledge Graph operations when available in the workspace catalog.
• Building multi-workflow pipelines that source entities, store them in shared Knowledge Graph lists, qualify them, and route approved results to later workflows.
• Applying approval gates to writes and using scheduled triggers for recurring, idempotent processing.

Before selecting an app or data source, agents should inspect the available app catalog and action schemas. Some connectors require the user’s own connected account or API key, while other services consume Agentled credits.

Limitations and notes

JavaScript is supported for code steps; Python is not. Workflow step types and fields are schema-controlled, and unknown fields may be stripped when saved. Full step-array updates are intended for imports and round-trips; interactive edits should use per-step tools. Dictionary-shaped fields should be read before replacement to avoid unintentionally removing sibling values.

Deleting a workflow is a two-phase operation: the first call creates a confirmation token and the second performs the cascade delete. Snapshot limits depend on the Agentled plan. Credit totals should be reported with their returned period label and date range. The server requires an Agentled API key, and actual integrations, models, and data sources depend on the workspace catalog and connected accounts.