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retell_create_conversation_flow

Create a structured conversation flow to control AI agent dialogue, using nodes for conversation, function calls, branching, and end conditions.

Instructions

Create a new Conversation Flow. A Conversation Flow is a node-graph that controls agent dialogue with conversation nodes, function nodes, branch nodes, and end nodes connected by edges.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nodesYesArray of nodes in the flow. Each node must have an 'id', a 'type' (e.g. 'conversation', 'end', 'function', 'transfer_call', 'branch'), and relevant fields. Conversation nodes also need an 'instruction' object with 'type' ('prompt' or 'static_text') and 'text'.
toolsNoReusable tool/function definitions available in this flow.
model_choiceYesThe LLM model choice. Example: { "type": "cascading", "model": "gpt-4.1" }. Supported models: gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, claude-4.5-sonnet, claude-4.5-haiku, gemini-2.5-flash.
global_promptNoA global prompt applied to every node in the conversation flow.
start_node_idNoID of the node where the conversation begins.
start_speakerYesWho speaks first — 'agent' or 'user'.
knowledge_base_idsNoKnowledge base IDs for RAG retrieval.
default_dynamic_variablesNoDefault values for dynamic variables used in the flow.
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

There are no annotations, so the description carries the burden. It adds useful domain context (node types, edges, dialogue control) but does not disclose behavioral traits like validation rules, return values, or side effects beyond the obvious 'create' mutation. The information is accurate but skeletal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose and followed by a clear definition. Every word earns its place; no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is complex (8 params, nested objects) and there is no output schema, so more context would help. The description defines the core concept but omits expected return value, validation behaviors, and relationship to conversation flow components. Still, the schema compensates heavily for parameter details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds a bit of meaning by mentioning node types and edges, which complements the nodes parameter definition, but it adds little beyond what the schema already states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Create a new Conversation Flow' with a specific verb and resource, and clarifies what a Conversation Flow is (a node-graph controlling agent dialogue). This clearly distinguishes it from other create tools for agents, LLMs, phone numbers, etc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like update_conversation_flow, delete_conversation_flow, or create_conversation_flow_component. There are no mentions of prerequisites (e.g., needing an LLM or knowledge base) or workflows such as attaching the created flow to an agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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