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retell_update_llm

Modify an AI agent's language model configuration by updating its base model, system prompts, and greeting messages to adjust conversation behavior.

Instructions

Update a Retell LLM configuration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
llm_idYesThe LLM configuration ID to update
modelNoNew base model
general_promptNoNew system prompt
begin_messageNoNew greeting message

Implementation Reference

  • src/index.ts:762-786 (registration)
    Tool registration in the tools array, defining name, description, and input schema for retell_update_llm.
    {
      name: "retell_update_llm",
      description: "Update a Retell LLM configuration.",
      inputSchema: {
        type: "object",
        properties: {
          llm_id: {
            type: "string",
            description: "The LLM configuration ID to update"
          },
          model: {
            type: "string",
            description: "New base model"
          },
          general_prompt: {
            type: "string",
            description: "New system prompt"
          },
          begin_message: {
            type: "string",
            description: "New greeting message"
          }
        },
        required: ["llm_id"]
      }
  • Specific handler logic within the executeTool switch statement that destructures args, extracts llm_id, and makes a PATCH request to the Retell API endpoint /update-retell-llm/{llm_id} with the update data.
    case "retell_update_llm": {
      const { llm_id, ...llmUpdateData } = args;
      return retellRequest(`/update-retell-llm/${llm_id}`, "PATCH", llmUpdateData as Record<string, unknown>);
    }
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Update' implies a mutation operation, the description doesn't disclose important behavioral traits: whether this requires specific permissions, whether changes are reversible, what happens to existing configuration fields not mentioned, or what the response looks like. For a mutation tool with zero annotation coverage, this is a significant gap.

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 states the core purpose without any wasted words. It's appropriately sized for a straightforward update operation and gets directly to the point with no unnecessary elaboration.

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?

For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address behavioral aspects like permissions, side effects, or response format. While the schema covers parameters well, the overall context for using this tool safely and effectively is insufficient given its complexity as an update operation.

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?

The description provides no parameter information beyond what's already in the schema. However, with 100% schema description coverage, all four parameters (llm_id, model, general_prompt, begin_message) are well-documented in the schema itself. The baseline score of 3 reflects adequate parameter documentation through the schema alone, though the description adds no additional semantic context.

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 verb ('Update') and resource ('a Retell LLM configuration'), making the purpose immediately understandable. It distinguishes this as an update operation rather than creation or deletion, though it doesn't explicitly differentiate from sibling update tools like retell_update_agent or retell_update_conversation_flow.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing LLM configuration), doesn't specify when this is appropriate versus creating a new LLM, and doesn't reference sibling tools for comparison or complementary operations.

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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