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list_models

Read-onlyIdempotent

View available AI voice models and per-minute pricing to compare options and manage costs for your phone agents.

Instructions

List available AI models for phone agents with their pricing per minute.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The actual handler function that executes the list_models tool - makes GET request to /models endpoint via client
    async () => callTool(() => client.get("/models"))
  • Input/output schema definition for list_models tool - empty input schema (no parameters required), with annotations indicating it's read-only, non-destructive, and idempotent
    {
      description: "List available AI models for phone agents with their pricing per minute.",
      inputSchema: {},
      annotations: { readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: false },
    },
  • Registration of the list_models tool within registerModelTools function - binds the tool name to its schema and handler
    export function registerModelTools(server: McpServer, client: BubblyPhoneClient) {
      server.registerTool(
        "list_models",
        {
          description: "List available AI models for phone agents with their pricing per minute.",
          inputSchema: {},
          annotations: { readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: false },
        },
        async () => callTool(() => client.get("/models"))
      );
    }
  • Helper function that wraps tool execution with error handling - catches ApiError and returns formatted toolError or toolResult
    async function callTool<T>(fn: () => Promise<T>) {
      try {
        return toolResult(await fn());
      } catch (err) {
        const apiErr = err as ApiError;
        return toolError(`API error (${apiErr.status}): ${apiErr.message}`);
      }
    }
  • BubblyPhoneClient.get() method - performs authenticated HTTP GET requests to the API, used by the list_models handler to fetch models data
    async get<T = unknown>(path: string, params?: Record<string, string>): Promise<T> {
      const url = new URL(`${this.baseUrl}${path}`);
      if (params) {
        for (const [key, value] of Object.entries(params)) {
          if (value !== undefined && value !== "") {
            url.searchParams.set(key, value);
          }
        }
      }
      return this.request<T>(url.toString(), { method: "GET" });
    }
Behavior3/5

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

Annotations already declare read-only, non-destructive, and idempotent properties. The description adds valuable context that the listing includes 'pricing per minute,' indicating what data fields to expect. However, it omits details like rate limits, caching behavior, or the full structure of returned model objects.

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 single-sentence description is efficiently front-loaded with the verb 'List' and packs essential information (resource type and key data field 'pricing') into 11 words without 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?

Given the absence of an output schema, the description partially compensates by mentioning 'pricing per minute,' but remains incomplete regarding other returned fields (model IDs, capabilities, supported languages). With zero input parameters and good annotations, the description meets minimum viability but could elaborate on return values.

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 input schema contains zero parameters. Per the rubric, 0 parameters establishes a baseline score of 4. The description correctly implies no filtering is needed by stating 'List available' without qualifying parameters.

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 ('List') and specific resource ('available AI models for phone agents'), distinguishing it from sibling 'list' tools like list_calls or list_phone_numbers by specifying the AI domain. However, it does not explicitly contrast with siblings or clarify selection criteria.

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, nor does it mention prerequisites (e.g., whether certain account types can access all models) or workflow context (e.g., 'use this before make_call to select a model').

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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