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retell_create_llm

Configure custom AI agents by setting model parameters, system prompts, and conversation tools for voice and chat interactions.

Instructions

Create a new Retell LLM configuration with custom prompts and settings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesThe base model (e.g., 'gpt-4o', 'claude-3.5-sonnet', 'gpt-4o-mini')
general_promptYesThe main system prompt defining the agent's behavior and personality
begin_messageNoOptional: The greeting message the agent says when call starts
general_toolsNoOptional: Array of tool configurations for function calling
inbound_dynamic_variables_webhook_urlNoOptional: Webhook URL to fetch dynamic variables for inbound calls
knowledge_base_idsNoOptional: Array of knowledge base IDs to use

Implementation Reference

  • The handler logic for the 'retell_create_llm' tool in the executeTool switch statement. It forwards the request to Retell's API endpoint /create-retell-llm.
    case "retell_create_llm":
      return retellRequest("/create-retell-llm", "POST", args);
  • src/index.ts:702-739 (registration)
    Registration of the 'retell_create_llm' tool in the tools array, including name, description, and inputSchema for validation.
    {
      name: "retell_create_llm",
      description: "Create a new Retell LLM configuration with custom prompts and settings.",
      inputSchema: {
        type: "object",
        properties: {
          model: {
            type: "string",
            description: "The base model (e.g., 'gpt-4o', 'claude-3.5-sonnet', 'gpt-4o-mini')"
          },
          general_prompt: {
            type: "string",
            description: "The main system prompt defining the agent's behavior and personality"
          },
          begin_message: {
            type: "string",
            description: "Optional: The greeting message the agent says when call starts"
          },
          general_tools: {
            type: "array",
            description: "Optional: Array of tool configurations for function calling",
            items: {
              type: "object"
            }
          },
          inbound_dynamic_variables_webhook_url: {
            type: "string",
            description: "Optional: Webhook URL to fetch dynamic variables for inbound calls"
          },
          knowledge_base_ids: {
            type: "array",
            items: { type: "string" },
            description: "Optional: Array of knowledge base IDs to use"
          }
        },
        required: ["model", "general_prompt"]
      }
    },
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states 'Create' implying a write/mutation operation, but doesn't disclose behavioral traits like required permissions, whether the creation is idempotent, rate limits, or what happens on failure. This is a significant gap for a mutation tool with zero annotation coverage.

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?

The description is a single, efficient sentence that front-loads the core purpose. Every word earns its place with no redundancy or fluff, making it easy to parse quickly.

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

Completeness2/5

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

Given this is a mutation tool (create) with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., LLM ID, success status), error conditions, or integration with other tools like retell_create_agent that might use LLM configurations.

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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond implying 'custom prompts and settings' aligns with parameters like general_prompt and model. Baseline 3 is appropriate when schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the action ('Create') and resource ('new Retell LLM configuration'), specifying it involves 'custom prompts and settings'. It distinguishes from siblings like retell_create_agent or retell_update_llm by focusing on LLM configurations specifically, though it doesn't explicitly contrast with them.

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. With many sibling tools (e.g., retell_create_agent, retell_update_llm), the description lacks context about prerequisites, dependencies, or scenarios where creating an LLM configuration is appropriate versus other creation tools.

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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