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Agentic Coding

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Actionforge provides two MCP servers that let AI coding assistants build, run, and debug action graphs.

WhatHow
☁️ Cloud MCP ServerBuild and validate graphs locallyRemote MCP server
🖥️ Local MCP ServerRun and debug graphs locallyactrun mcp over stdio

Both MCP servers interact with graphs that you have stored locally on your computer. But the cloud MCP server is solely used for creating and editing them, and the Local MCP Server uses a local installation of actrun to run and debug these graphs.

The cloud MCP server gives AI agents the tools to create, validate, and explore Actionforge graphs. It connects to the Actionforge knowledge base for node search, documentation lookup, and example graph retrieval.

Endpoint: https://app.actionforge.dev/api/v2/mcp

Terminal window
claude mcp add --transport http actionforge https://app.actionforge.dev/api/v2/mcp
Available Tools
ToolDescription
get_graph_schemaFull JSON schema for .act graph files
search_nodesSearch available node types by keyword or category
describe_nodeDetailed documentation for a specific node type
get_node_schemaFull JSON schema for specific node types
get_port_typesPort data types and connection rules
validate_graphValidate a complete graph structure
search_graph_examplesFind example graphs and recommended nodes for a task
search_documentationSearch the Actionforge knowledge base (CLI docs, API references, general docs)

The actrun CLI includes a built-in MCP server for running and debugging graphs locally. It communicates over stdio and exposes debug tools that let an AI agent step through graph execution node by node, set breakpoints, and inspect state.

Terminal window
claude mcp add actrun -- actrun mcp
Available Tools
ToolDescription
debug_connectConnect to a local actrun debug server
debug_runSend a graph and start execution
debug_stepStep over the current node
debug_step_intoStep into a group node
debug_step_outStep out of a group node
debug_resumeResume until breakpoint or completion
debug_pausePause at the next node
debug_set_breakpointSet a breakpoint at a node
debug_remove_breakpointRemove a breakpoint
debug_inspectInspect current debug state
debug_logsRetrieve buffered log output
debug_stopStop the current execution
debug_disconnectClose the debug connection

The AI agent follows this flow automatically:

  1. Start actrun --local in the background (captures the WebSocket port)
  2. Call debug_connect with the port
  3. Read the .act file from disk and send it via debug_run
  4. Step through execution with debug_step / debug_step_into / debug_resume
  5. Inspect state and logs at each pause point
  6. Call debug_disconnect and stop the background process when done