Agent commands. Your AI agent uses this node in workflows. You manage the MCP servers that provide these tools with
pflow mcp commands.How it works
- Add an MCP server with
pflow mcp add - Sync tools with
pflow mcp sync(or let auto-sync handle it) - Tools become nodes named
mcp-{server}-{tool} - Agent uses them like any other node
Node naming
MCP tools become nodes with the patternmcp-{server}-{tool}:
| Server | Tool | Node name |
|---|---|---|
| github | create_issue | mcp-github-create_issue |
| slack | send_message | mcp-slack-send_message |
| filesystem | read_file | mcp-filesystem-read_file |
Parameters
MCP node parameters come directly from the tool’s input schema. Each MCP server defines its own tools with their own parameters. To see a tool’s parameters:Output
All MCP nodes write to:| Key | Type | Description |
|---|---|---|
result | any | Tool execution result |
{server}_{tool}_result | any | Same result with unique key |
error | str | Error message (only on failure) |
{"issue_url": "...", "issue_number": 123}, the shared store contains:
Example workflow
Setup
To use MCP tools, first add MCP servers to pflow. See Adding MCP servers for configuration format, authentication, and examples. pflow supports both local (stdio) and remote (HTTP) MCP servers. Both work identically in workflows - same naming, parameters, and output structure.Error handling
MCP nodes returnerror action on:
- Connection failure: Server not reachable
- Tool error: Tool reported an error (via
isErrorflag) - Timeout: Request took too long (default 30 seconds)
Auto-sync
When you run a workflow, pflow automatically syncs MCP tools if the server configuration has changed. You don’t need to manually runpflow mcp sync after adding servers.
Manual sync is useful for:
- Testing connection immediately after adding a server
- Debugging connection issues
- Forcing re-discovery without running a workflow

