griptape-nodes-workflows

name	griptape-nodes-workflows
description	Build, run, and inspect Griptape Nodes workflows by driving the engine's MCP server. Use when the user asks to construct a node workflow, run an existing one, set parameter values, wire connections between nodes, or read output from a workflow run. Triggers include "build a workflow", "add a node to the flow", "connect these nodes", "run the flow", "what does node X output".

Griptape Nodes Workflow Construction Guide

This skill covers the full cold-start cycle (build → wire → run → read) against the engine's MCP server, plus the idioms and gotchas discovered from running real workflows.

Mental Model

• Workflow: Top-level namespace. Only ONE can be active at a time. Reset with ClearAllObjectStateRequest.
• Flow: The canvas inside a workflow. A workflow has exactly one top-level "canvas" flow. Sub-flows are possible but rarely needed for scratch work.
• Node: A unit of work with parameters (inputs, outputs, properties).
• Connection: An edge between two parameters. Two kinds:
  • Data flow: A typed parameter on one node → a typed parameter on another. The engine derives execution order from data dependencies, so data connections alone are usually sufficient.
  • Control flow: exec_out → exec_in. Only needed when two nodes must run in a specific order but don't share data (e.g. side-effecting steps, branching). Skip these by default.
• Current Context: A stack (workflow → flow → node). Most requests default to "current" when a name is omitted.

What the MCP server actually exposes

Every MCP tool corresponds 1:1 to a RequestPayload class registered in SUPPORTED_REQUEST_EVENTS (src/griptape_nodes/servers/mcp.py). The server name is prefixed onto each tool, so the request CreateNodeRequest is reachable as griptape_nodes_CreateNodeRequest. A few consequences worth remembering up front:

• There are no plural variants of individual requests. CreateNodesRequest, CreateConnectionsRequest, etc. do not exist. To create N nodes, send N CreateNodeRequest calls — or wrap them in a single EventRequestBatch call (see below).
• CreateNodeRequest does not take parameter values. Setting a parameter is always a separate SetParameterValueRequest after the node exists. There is no parameter_values / inputs shortcut on create.
• EventRequestBatch is the only fan-out primitive. It is a synthetic tool (no matching RequestPayload class) that ships an ordered list of inner requests in one transport frame. Reach for it whenever you already know the shape of a build phase. See "EventRequestBatch: collapse a build phase into one round trip" below.

Survey the Workspace First

Before building anything, find out what is actually loaded in this engine. The set of registered libraries and the node types they expose is the catalog every later step draws from, so spend the round trips up front instead of guessing names that may not exist (or that exist in a different library than you expect).

Find the workspace directory on disk

The MCP surface does not expose GetConfigValueRequest, so resolve the workspace path by reading the user config file directly. On macOS / Linux it lives at:

~/.config/griptape_nodes/griptape_nodes_config.json

The keys you care about are:

• workspace_directory: absolute (or ~-prefixed) workspace root. The sandbox library lives inside this directory.
• app_events.on_app_initialization_complete.libraries_to_register: list of locally registered griptape_nodes_library.json paths. Entries can be either a string or an object {"path": "...", "enabled": true}. Reading these tells you where each library's source lives if you need to look at an existing node's Python module before writing a similar sandbox node.

The sandbox subdirectory key (sandbox_library_directory) is optional and defaults to sandbox_library. The absolute sandbox path is therefore <workspace_directory>/<sandbox_library_directory> (e.g. ~/Projects/.../GriptapeNodes/sandbox_library).

Do this once per session and remember the paths; they don't change mid-run.

Survey the registered libraries via MCP

A. griptape_nodes_ListRegisteredLibrariesRequest()
   → list of library names currently loaded (e.g. "Griptape Nodes Library",
     "Sandbox Library"). If a library you expect is missing, no `DescribeNodeType`
     call against its node types will resolve.

B. griptape_nodes_ListNodeTypesInLibraryRequest(library="<name>")
   → call once per library you might pull from. The returned node-type names are the
     exact strings to pass to `CreateNodeRequest.node_type` and
     `DescribeNodeTypeRequest.node_type`.

C. (optional) griptape_nodes_ListCategoriesInLibraryRequest(library="<name>")
   → useful when you only care about a slice of a large library (e.g. only image
     nodes) and want to scope the next step.

Do this once per session, then reuse the catalog. Skip the survey only when you already know (from a prior call in the same session) which library provides each node type you intend to use.

EventRequestBatch: collapse a build phase into one round trip

EventRequestBatch (MCP tool name: griptape_nodes_EventRequestBatch) wraps an ordered list of inner requests in a single transport frame. Use it whenever the shape of the build phase is already decided — typical pattern is N CreateNodeRequest

• M SetParameterValueRequest + K CreateConnectionRequest + one AutoLayoutFlowRequest, all in one round trip.

Shape:

{
  "requests": [
    {"request_type": "CreateNodeRequest",
     "request": {"node_type": "TextInput", "node_name": "TextInput_1"}},
    {"request_type": "SetParameterValueRequest",
     "request": {"node_name": "TextInput_1", "parameter_name": "text", "value": "..."}}
  ],
  "timeout_ms": 60000
}

Behavior:

• Sequential dispatch. The engine awaits inner requests in submission order (for inner in batch.requests: await _dispatch_event_request(inner)), so a CreateNodeRequest followed by a SetParameterValueRequest on that same node is safe inside one batch.
• Pre-flight validation. Every inner request is constructed against its RequestPayload class before anything goes on the wire. An unknown request_type, a request that is not a JSON object, or unknown kwargs in the inner payload all reject the entire batch up front with a TypeError / ValueError.
• Per-slot failure isolation. Once the batch is dispatched, a failure in one slot does not abort siblings. Failed slots come back as {"ok": false, "details": "..."} in the result array; successful slots come back as the same flattened object a single tool call would return. Walk every slot before assuming the batch succeeded.
• No nesting. EventRequestBatch is intentionally absent from SUPPORTED_REQUEST_EVENTS and the inner request_type enum, so a batch cannot contain another batch.
• Default timeout scales with size. timeout_ms defaults to 30000 × len(requests) clamped at 300000 ms (5 min). Pass an explicit override when the last slot is StartFlowRequest(wait_for_completion=True) or any other long-running call; otherwise the synchronous run can eat the budget meant for the rest of the batch. bool is rejected explicitly so True cannot silently become 1ms.

Return shape: a JSON array of trimmed slot responses in submission order. Each slot looks identical to the response that single-tool dispatch would have returned for that request_type.

[
  {"ok": true, "node_name": "TextInput_1", "...": "..."},
  {"ok": true, "finalized_value": "...", "...": "..."}
]

Critical idiom: pre-name nodes you reference later in the same batch

Inside one batch you cannot read CreateNodeResultSuccess.node_name from an earlier slot before composing a later one — every entry is fixed when the batch is submitted. So either:

• Pass an explicit node_name on every CreateNodeRequest and reuse those names verbatim in later SetParameterValueRequest / CreateConnectionRequest entries, or
• Split the build across two batches: one that creates nodes (read the assigned names back from the result array), then a second that sets parameters and wires them up.

The one-batch + explicit names path is almost always shorter and is what the batched recipe below uses.

Canonical Cold-Start Recipe

For a typical 3-node linear pipeline (TextInput → Agent → DisplayText), once the workspace survey above has confirmed the relevant library exposes those node types:

1. griptape_nodes_EnsureWorkflowAndFlowRequest()
   → returns workflow_name, flow_name, created_workflow, created_flow.
     Idempotent: if both pieces are already in context, reuses them.

2. griptape_nodes_DescribeNodeTypeRequest(node_type="TextInput")
3. griptape_nodes_DescribeNodeTypeRequest(node_type="Agent")
4. griptape_nodes_DescribeNodeTypeRequest(node_type="DisplayText")
   → get exact parameter names/types/modes. Look for:
     - data input(s)  (mode_allowed_input  == true)
     - data output(s) (mode_allowed_output == true)
     - control params (type == "parametercontroltype"): exec_in / exec_out
       are usually skippable (see Mental Model)

5. griptape_nodes_CreateNodeRequest(node_type="TextInput")
6. griptape_nodes_CreateNodeRequest(node_type="Agent")
7. griptape_nodes_CreateNodeRequest(node_type="DisplayText")
   → each returns a flat `node_name` (e.g. "TextInput_1", "Agent_1"). Read it from
     the response, do not assume a naming convention. The default name comes from
     `metadata.display_name` and may include spaces (e.g. "Text Input_1"). Pass an
     explicit `node_name` if you want a stable handle.

8. griptape_nodes_SetParameterValueRequest(
       node_name="TextInput_1", parameter_name="text", value="...")
   → there is no `parameter_values` shortcut on CreateNode; set every non-default
     parameter with its own SetParameterValue call.

9. griptape_nodes_CreateConnectionRequest(
       source_node_name="TextInput_1",   source_parameter_name="text",
       target_node_name="Agent_1",       target_parameter_name="prompt")
10. griptape_nodes_CreateConnectionRequest(
       source_node_name="Agent_1",       source_parameter_name="output",
       target_node_name="DisplayText_1", target_parameter_name="text")
    → data connections only; the engine orders execution from data dependencies.

11. griptape_nodes_AutoLayoutFlowRequest()
    → required after any multi-node build. Without it, every node lands at (0, 0)
      and the canvas shows them stacked on top of each other. Topologically sorts
      the graph and assigns column-and-row positions. Omit `flow_name` to lay out
      the current-context flow.

12. griptape_nodes_StartFlowRequest(wait_for_completion=True, completion_timeout_ms=60000)
    → omit flow_name; the handler uses the current-context flow.
      wait_for_completion blocks until the flow resolves or times out.

13. griptape_nodes_GetParameterValueRequest(node_name="DisplayText_1", parameter_name="text")
    → the terminal node's output.

Thirteen MCP calls: 1 ensure + 3 describe + 3 create + 1 set + 2 connect + 1 layout + 1 run + 1 read. Wider graphs scale linearly: every node adds one CreateNode plus its SetParameterValue calls; every edge adds one CreateConnection.

Batched variant (4 round trips)

The build phase (steps 5-11 above) has fixed shape, so it collapses into one EventRequestBatch call. The describe phase still benefits from being a separate batch because its results inform the build payload, and StartFlowRequest is usually kept out of the build batch so its long timeout does not gate the rest:

1. EnsureWorkflowAndFlowRequest                        (1 call)
2. EventRequestBatch([                                  (1 call, runs sequentially)
     DescribeNodeTypeRequest("TextInput"),
     DescribeNodeTypeRequest("Agent"),
     DescribeNodeTypeRequest("DisplayText"),
   ])
3. EventRequestBatch([                                  (1 call, runs sequentially)
     CreateNodeRequest(node_type="TextInput",   node_name="TextInput_1"),
     CreateNodeRequest(node_type="Agent",       node_name="Agent_1"),
     CreateNodeRequest(node_type="DisplayText", node_name="DisplayText_1"),
     SetParameterValueRequest(node_name="TextInput_1", parameter_name="text", value="..."),
     CreateConnectionRequest(source_node_name="TextInput_1",   source_parameter_name="text",
                             target_node_name="Agent_1",       target_parameter_name="prompt"),
     CreateConnectionRequest(source_node_name="Agent_1",       source_parameter_name="output",
                             target_node_name="DisplayText_1", target_parameter_name="text"),
     AutoLayoutFlowRequest(),
   ])
4. StartFlowRequest(wait_for_completion=True, completion_timeout_ms=60000)
   + GetParameterValueRequest("DisplayText_1", "text")            (2 calls)

Four round trips instead of thirteen. Walk the result arrays after each batch and verify every slot returned ok: true before moving on; per-slot failures don't abort the rest of the batch, so a typo in slot 4 still lets slots 5 and 6 run against stale state.

Key Idioms

• Survey the workspace before picking node types. Read the JSON config at ~/.config/griptape_nodes/griptape_nodes_config.json to learn where the sandbox directory and registered library JSONs live, then run ListRegisteredLibrariesRequest and ListNodeTypesInLibraryRequest for the libraries you care about before reaching for DescribeNodeType. The catalog tells you which node types actually exist in this engine and which library owns each one, which is the input both DescribeNodeTypeRequest.library and CreateNodeRequest.specific_library_name expect when the same name lives in more than one library.
• Discover before wiring. Always call DescribeNodeType for each node type you intend to use before guessing parameter names. The cost is 3-5 calls up front but saves many round trips fighting typos and assumed-wrong names.
• Batch with EventRequestBatch once the shape is known. Anything past the discovery phase usually has fixed shape (N creates + M sets + K connects + a layout). Wrap them in one EventRequestBatch call and pre-name every node you reference later in the same batch. Inspect every slot of the result array; per-slot failures do not abort siblings. Keep StartFlowRequest out of the build batch unless you raise timeout_ms to cover the synchronous run.
• Wire data, not control. The engine derives execution order from data dependencies, so exec_out → exec_in connections are usually noise. Only add them when two nodes must run in a specific order but don't exchange data.
• Always run AutoLayout after a multi-node build. Without it nodes land at (0, 0) and stack on top of each other. AutoLayoutFlowRequest is one round trip and idempotent; treat it as the closing step of any build phase.
• Use wait_for_completion=True on StartFlowRequest. For workflows that touch LLMs, image generators, or long I/O, set completion_timeout_ms generously (60000+ ms). Otherwise the call returns the instant the flow is kicked off and you have to poll GetNodeResolutionStateRequest yourself.
• Omit flow_name on StartFlowRequest when you just finished building a single flow. The handler defaults to the current-context flow.
• Read the response, don't assume names. CreateNodeResultSuccess.node_name is the authoritative handle for every later call. Pass it verbatim into SetParameterValueRequest, CreateConnectionRequest, and GetParameterValueRequest.

Response Shape

Every MCP tool returns a trimmed object (the engine envelope is unwrapped server-side in _trim_response):

{
  "ok": true,
  "details": "<human-readable summary, may be omitted>",
  "altered_workflow_state": true|false,
  "...": "...payload fields..."
}

ok reflects whether the engine produced a *ResultSuccess payload. The remaining fields are flattened from the inner result class, so for example CreateNodeResultSuccess.node_name is reachable as response["node_name"], EnsureWorkflowAndFlowResultSuccess exposes workflow_name, flow_name, created_workflow, created_flow at the top level, and AutoLayoutFlowResultSuccess exposes flow_name and positioned_nodes. Failures surface as MCP tool errors with the engine's result_details message attached.

EventRequestBatch returns a JSON array instead of a single object. Each slot mirrors the single-call shape above, so a batched build phase looks like [{"ok": true, "node_name": "TextInput_1", ...}, {"ok": true, ...}, ...]. Failed slots come back as {"ok": false, "details": "..."} in place; the rest of the array still executes.

Gotchas

DescribeNodeType may be partial

DescribeNodeType probes by instantiating the node class. For node types whose __init__ performs I/O (network, auth, disk), instantiation may fail. When that happens the response still succeeds but returns:

• Full library-level metadata (category, description, display_name, etc.)
• parameters: [] (empty — instantiation failed before parameters were declared)
• A WARNING-level entry in details naming the cause

You still know what the node does, but you cannot see its parameter schema from MCP alone. Consider falling back to a different node type, or describe on a system where credentials are present.

CreateNode can silently produce an ErrorProxyNode

When a node fails to instantiate and create_error_proxy_on_failure=True (the default), the engine substitutes an ErrorProxyNode and reports success. Inspect the response's node_type (it will be the proxy class) or the details string if a later step fails mysteriously. If you need strict failure semantics, set create_error_proxy_on_failure=False on the request.

Default node names contain spaces

The engine names nodes after metadata.display_name, which is often human-readable with spaces (e.g. "Text Input_1", "Display Text_1"). That works for every API, but it's easy to typo. Either pass an explicit node_name per request, or always read the returned node_name from CreateNodeResultSuccess and reuse it verbatim.

Connection request field names are long

CreateConnectionRequest uses the unabbreviated names source_node_name, source_parameter_name, target_node_name, target_parameter_name. There are no short aliases (source, source_param, etc.). A typo here surfaces as an opaque validation error from pydantic, not a friendly "unknown field" message.

Only one workflow in context at a time

SetWorkflowContextRequest refuses if a workflow is already in context. To swap, ClearAllObjectStateRequest(i_know_what_im_doing=True) first — this wipes EVERYTHING (nodes, flows, connections, workflow). There is no softer reset today.

Agents cannot be interrupted mid-run

There is no pause/cancel for a running flow today. Use completion_timeout_ms to bound the wait; if the timeout fires, StartFlowRequest returns a failure but the flow keeps running in the engine until it finishes or errors. A subsequent StartFlowRequest will fail with "Flow is already running" until it does.

Tool Cheat Sheet

Goal	Tool
Bootstrap a workflow + flow from cold	EnsureWorkflowAndFlowRequest
Fan N requests out in one round trip	EventRequestBatch (synthetic; pre-name nodes that later slots reference)
Discover libraries / node types	ListRegisteredLibrariesRequest, ListNodeTypesInLibraryRequest, ListCategoriesInLibraryRequest
Inspect a node type's parameters	DescribeNodeTypeRequest
Create a node	CreateNodeRequest
Wire a single edge	CreateConnectionRequest
Lay out the canvas after a multi-node build	AutoLayoutFlowRequest
Move a single node to an explicit position	SetNodeMetadataRequest (set metadata.position)
Set a parameter value	SetParameterValueRequest
Read a parameter value	GetParameterValueRequest
Inspect a parameter's schema/details on a live node	GetParameterDetailsRequest, ListParametersOnNodeRequest
Run synchronously	StartFlowRequest(wait_for_completion=True, completion_timeout_ms=...)
Run from a specific node	StartFlowFromNodeRequest
Resolve a single node without firing the control flow	ResolveNodeRequest
Execute a single node directly	ExecuteNodeRequest
Rename a node or flow	RenameObjectRequest(allow_next_closest_name_available=True)
Lock or unlock a node	SetLockNodeStateRequest
Reset a node's parameters to defaults	ResetNodeToDefaultsRequest
Inspect state	ListNodesInFlowRequest, ListConnectionsForNodeRequest, GetNodeResolutionStateRequest, GetNodeMetadataRequest, GetConnectionsForParameterRequest
Find nodes by Python class (e.g. StartFlow, Agent)	ListNodesInFlowRequest(node_types=["StartFlow", "Agent"]) — returns only nodes whose class name matches; omit to get all nodes
Register a sandbox node type from Python source already on disk	RegisterSandboxNodeFromSourceRequest (see Custom nodes below)
Reset everything	ClearAllObjectStateRequest(i_know_what_im_doing=True)

Custom nodes

If the task involves writing a new node type via RegisterSandboxNodeFromSourceRequest, read the node development overview, the parameters reference, and the execution and lifecycle guide before drafting source. These pages document the engine-side conventions a sandbox class must follow:

• BaseNode subclassing and the process / aprocess contract
• Parameter declaration, modes (mode_allowed_input / ..._property / ..._output), traits
• ParameterString / ParameterImage / etc. helpers (preferred over hand-rolled Parameter)
• ParameterGroup / ParameterList containers
• Connection rules and node states

RegisterSandboxNodeFromSourceRequest only registers Python source already on disk inside the sandbox library directory; it never writes the file itself. The agent is responsible for placing the .py file under <workspace_directory>/<sandbox_library_directory> (via its own filesystem tool, e.g. pi's write) before issuing the request. The imported source then runs inside the engine process with no isolation, so matching the conventions up front is faster than iterating on registration failures. For pure workflow-driving tasks (build → wire → run → read) the guides are overkill — stick to this skill.

Example: One-Shot Haiku Pipeline

Goal: run an Agent on a one-line prompt and read the output.

1. EnsureWorkflowAndFlowRequest()
2. DescribeNodeTypeRequest(node_type="TextInput") → text output parameter is text
3. DescribeNodeTypeRequest(node_type="Agent") → input prompt; output output
4. DescribeNodeTypeRequest(node_type="DisplayText") → input text
5. CreateNodeRequest(node_type="TextInput") → read assigned node_name
6. CreateNodeRequest(node_type="Agent") → read assigned node_name
7. CreateNodeRequest(node_type="DisplayText") → read assigned node_name
8. SetParameterValueRequest(node_name="TextInput_1", parameter_name="text", value="Write a haiku about clouds.")
9. CreateConnectionRequest(TextInput_1.text → Agent_1.prompt)
10. CreateConnectionRequest(Agent_1.output → DisplayText_1.text)
11. AutoLayoutFlowRequest() → arrange the 3 nodes across columns
12. StartFlowRequest(wait_for_completion=True, completion_timeout_ms=60000)
13. GetParameterValueRequest(node_name="DisplayText_1", parameter_name="text")

Total: 13 MCP calls from empty engine to rendered output.

Same pipeline, batched (4 round trips)

1. EnsureWorkflowAndFlowRequest()
2. EventRequestBatch([DescribeNodeTypeRequest × 3])
3. EventRequestBatch([CreateNodeRequest × 3 (with explicit node_name), SetParameterValueRequest, CreateConnectionRequest × 2, AutoLayoutFlowRequest])
4. StartFlowRequest(wait_for_completion=True, completion_timeout_ms=60000) then GetParameterValueRequest("DisplayText_1", "text")

The build batch in step 3 only works because every CreateNodeRequest carries an explicit node_name; the later SetParameterValueRequest and CreateConnectionRequest slots reference those names directly instead of waiting on the per-create response.

Further reading

For anything beyond the build → wire → run → read scope of this skill, start at /for_agents/. It is the canonical entry point to the engine's machine-readable doc surface — it explains the trade-offs between /llms.txt (curated index), /llms-full.txt (full corpus in one fetch), and per-page .md files, and lists the highest-value pages for grounding an agent in the engine's actual API.