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retell_create_batch_test

Run automated test scenarios against a Retell AI agent to validate performance and conversation flows.

Instructions

Run automated test scenarios against an agent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesThe agent ID to test
test_casesYesArray of test case configurations

Implementation Reference

  • Schema definition for the retell_create_batch_test tool, including input schema for agent_id and test_cases.
    {
      name: "retell_create_batch_test",
      description: "Run automated test scenarios against an agent.",
      inputSchema: {
        type: "object",
        properties: {
          agent_id: {
            type: "string",
            description: "The agent ID to test"
          },
          test_cases: {
            type: "array",
            description: "Array of test case configurations"
          }
        },
        required: ["agent_id", "test_cases"]
      }
    },
  • Handler case in executeTool function that executes the tool by calling retellRequest to the /create-batch-test API endpoint.
    case "retell_create_batch_test":
      return retellRequest("/create-batch-test", "POST", args);
  • Generic retellRequest helper function that performs authenticated API calls to Retell AI backend, used by all tool handlers.
    async function retellRequest(
      endpoint: string,
      method: string = "GET",
      body?: Record<string, unknown>
    ): Promise<unknown> {
      const apiKey = getApiKey();
    
      const headers: Record<string, string> = {
        "Authorization": `Bearer ${apiKey}`,
        "Content-Type": "application/json",
      };
    
      const options: RequestInit = {
        method,
        headers,
      };
    
      if (body && method !== "GET") {
        options.body = JSON.stringify(body);
      }
    
      const response = await fetch(`${RETELL_API_BASE}${endpoint}`, options);
    
      if (!response.ok) {
        const errorText = await response.text();
        throw new Error(`Retell API error (${response.status}): ${errorText}`);
      }
    
      // Handle 204 No Content
      if (response.status === 204) {
        return { success: true };
      }
    
      return response.json();
    }
  • src/index.ts:1283-1285 (registration)
    Registration of the tool list handler, which exposes the tools array (including retell_create_batch_test) via MCP ListToolsRequest.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      return { tools };
    });
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. It states the tool runs automated tests but doesn't explain what 'test scenarios' entail, whether this is a read-only or mutating operation, potential side effects, rate limits, or expected output format. This leaves significant gaps for an agent to understand the tool's behavior.

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 with no wasted words. It's appropriately sized and front-loaded, clearly stating the tool's purpose without 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?

Given the complexity of running automated tests (which could involve mutations, side effects, or specific outputs) and the lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects, return values, or usage context, making it inadequate for an agent to fully understand the tool's role.

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 input schema has 100% description coverage, documenting both parameters ('agent_id' and 'test_cases'). The description doesn't add any meaning beyond this, such as clarifying what constitutes a test case configuration or how the agent ID is used. Baseline 3 is appropriate when the 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 ('Run automated test scenarios') and target ('against an agent'), providing a specific verb+resource combination. However, it doesn't differentiate this from sibling tools like 'retell_create_batch_call' or 'retell_create_chat_completion', which might also involve testing or batch operations.

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. The description doesn't mention prerequisites, context, or exclusions, such as whether this is for pre-deployment validation, regression testing, or how it differs from other batch or test-related operations in the sibling list.

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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