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retell_list_llms

Retrieve available AI language model configurations for building voice agents on the Retell AI platform.

Instructions

List all Retell LLM configurations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler logic for the 'retell_list_llms' tool within the executeTool switch statement. It performs a GET request to the Retell API endpoint '/list-retell-llms'.
    case "retell_list_llms":
      return retellRequest("/list-retell-llms", "GET");
  • The tool descriptor defining the name, description, and input schema (empty object, no parameters required). This schema is used for validation and tool listing.
    {
      name: "retell_list_llms",
      description: "List all Retell LLM configurations.",
      inputSchema: {
        type: "object",
        properties: {}
      }
    },
  • src/index.ts:1283-1285 (registration)
    Registration of the tool listing handler that exposes the tools array (including retell_list_llms) via MCP protocol.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      return { tools };
    });
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 a read operation ('List'), implying it's non-destructive, but doesn't disclose behavioral traits like pagination, rate limits, authentication needs, or output format. This is a significant gap for a 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 directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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?

Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate but lacks completeness. It doesn't cover behavioral aspects like output format or usage context, which are important even for simple tools, leaving gaps in understanding.

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

Parameters4/5

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

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the inputs. The description adds no parameter information, which is acceptable given the baseline of 4 for zero parameters, as it doesn't need to compensate for any gaps.

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 ('List') and resource ('all Retell LLM configurations'), making the purpose specific and understandable. It distinguishes from siblings like 'retell_get_llm' (which fetches a single LLM) by indicating it retrieves multiple items, though it doesn't explicitly compare to other list tools like 'retell_list_agents'.

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, context for listing LLMs, or differentiate from other list tools in the sibling set, leaving the agent to infer usage based on the name alone.

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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