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create_agent

Creates a new AI voice agent for your organization with configurable language, voice, and model settings. Returns the agent ID for further configuration.

Instructions

Create a new AI agent in your organization. The agent is created as a single_prompt agent with gpt-4.1 model and daniel voice (waves_lightning_v3_1) by default. Returns the created agent's ID. Set the agent prompt via update_agent_prompt after creation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoName for the new agent
descriptionNoShort description of what the agent does
languageNoLanguage configuration. Defaults to English.
synthesizerNoVoice synthesizer configuration
slm_modelNoLLM model for the agent. Defaults to gpt-4.1.
global_promptNoGlobal system prompt for the agent (max 4000 chars). For the main prompt, use update_agent_prompt after creation.
first_messageNoFirst message the agent says when a call starts (max 500 chars)
default_variablesNoDefault template variables for the agent prompt (e.g. { company_name: 'Acme' })
knowledge_base_idNoKnowledge base ID to attach to the agent
allow_inbound_callNoWhether to allow inbound calls (default true)
allow_interruptionsNoWhether to allow user interruptions (default true)
wait_for_user_to_speak_firstNoWait for user to speak before agent starts (default false)
smart_turn_configNoSmart turn detection configuration
voicemail_detectionNoVoicemail detection configuration
background_soundNoBackground sound during calls
pronunciation_dictsNoCustom pronunciation dictionary
enable_style_guideNoEnable conversational style guide (default true)
Behavior4/5

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

Despite no annotations, the description discloses defaults (single_prompt, gpt-4.1, daniel voice) and the return value (created agent ID). It does not cover side effects like billing or limits, but for a creation tool, this is reasonable transparency.

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 two sentences, front-loading the purpose and defaults, and then adding the return value and prompt flow. No wasted words.

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

Completeness4/5

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

For a complex tool with 17 parameters and no output schema, the description covers the essential behavioral aspects and workflow. It could mention potential limitations like duplicate names or required permissions, but overall it is adequate.

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 has 100% coverage with descriptions, so the burden on the description is lower. The description adds value by stating defaults for model and voice and clarifying the prompt setup, which goes beyond the 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 clearly states the verb 'Create' and the resource 'new AI agent in your organization', distinguishing it from sibling tools like update_agent_config and update_agent_prompt. It also specifies defaults, adding clarity.

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?

The description tells when to use the tool (to create an agent) and implies a workflow by indicating that the prompt should be set via update_agent_prompt after creation. It does not explicitly list exclusions or alternatives, but the context is clear.

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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