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Neuratel-AI

Neuratel MCP Server

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by Neuratel-AI

make_call

Place an outbound call using a voice AI agent that autonomously manages the conversation with the recipient. Specify the agent, destination number, and caller ID to start.

Instructions

Place an outbound phone call using a voice AI agent.

This connects a real phone call between your AI agent and the destination number. The agent handles the entire conversation autonomously using its configured instructions and voice.

Prerequisites

  1. An agent must exist (use create_agent or list_agents to find one)

  2. A phone number must be provisioned (use list_numbers to find one)

  3. Account must have sufficient balance (use get_balance to check)

Minimum required

Just three fields: which agent talks, who to call, and which number to call from. Everything else is optional.

Per-call customization

Two powerful ways to customize each call without changing the agent:

dynamic_variables — inject values into the agent's prompt template. If the agent's instructions contain {{customer_name}}, pass: dynamic_variables={"customer_name": "Alice"}

agent_override — deep-merge config changes over the saved agent for this call only. The agent itself is not modified. Use this to:

  • Change the prompt for a specific call

  • Use a different voice or language

  • Adjust temperature for a sensitive conversation

  • Override any config section (brain, voice, transcriber, etc.)

Example override:

{"brain": {"instructions": "Special prompt for this call only"}}

Args: agent_id: The agent that will handle this call to_number: Destination in E.164 format (+12125551234) number_id: Your phone number ID to call from (from list_numbers) dynamic_variables: Template variables for the agent's prompt caller_id_name: Display name shown to the recipient (max 50 chars) caller_id_number: Override caller ID number (E.164). Defaults to the number_id's phone number if not set. agent_override: Per-call config overrides. Same structure as the agent config from get_agent. Deep-merged over the saved agent — only affects this call.

Returns: call_id for tracking via get_call, success status, and numbers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYes
number_idYes
to_numberYes
agent_overrideNo
caller_id_nameNo
caller_id_numberNo
dynamic_variablesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries the full burden and does well: it explains the call is handled autonomously, agent_override is a non-persistent deep-merge, and a balance prerequisite is mentioned implying cost. It could add explicit warnings about charges or call duration limits, but overall discloses key behavioral traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than average but well-structured with headers, bullet lists, and an example override JSON. The core action and prerequisites are front-loaded, and the length is justified by the tool's complexity. Some trimming of the customization section could improve conciseness, but it remains efficient.

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

Completeness5/5

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

Given the tool's complexity, 7 parameters, and existing output schema, the description covers prerequisites, required vs optional fields, per-call customization, and return values (call_id, success status, numbers). It references sibling tools for tracking and setup, making the description self-sufficient for proper invocation.

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

Parameters5/5

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

Schema coverage is 0%, but the description's Args section compensates richly: E.164 format, max 50 chars for caller_id_name, default behavior for caller_id_number, and detailed examples for dynamic_variables and agent_override with deep-merge semantics. Every parameter is given meaningful context beyond the raw schema.

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

Purpose5/5

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

The description opens with 'Place an outbound phone call using a voice AI agent,' which is a specific verb+resource statement that clearly distinguishes this from siblings like hangup_call, list_calls, and get_call. It unambiguously identifies the action and the object.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Prerequisites explicitly point to sibling tools (create_agent, list_agents, list_numbers, get_balance) and the 'Minimum required' section clarifies the essential fields. It stops short of explicitly saying 'when not to use' but provides clear context and alternatives via the prerequisite references.

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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