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Telnyx MCP Server

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update_assistant

Update an existing AI assistant by changing its name, model, instructions, tools, greeting, or telephony and messaging settings.

Instructions

Update an AI Assistant. Once there is an agent created, you can talk the user about what can be updated in an easy manner, rather than asking for a long list of fields to update.

Args: assistant_id: Required. ID of the assistant to update. name: Optional. Name of the assistant. model: Optional. Model to use for the assistant. instructions: Optional. Core instructions or behaviors for the agent. description: Optional. A summary of the agent's purpose. tools: Optional. List of tools for the assistant, each containing: - type: Required. Type of tool (ANY of "hangup", "retrieval", "send_dtmf", "transfer", "webhook"). - retrieval: Optional. For retrieval tools, contains: - bucket_ids: Required. List of bucket IDs for retrieval. - max_num_results: Optional. Maximum number of results to retrieve. - webhook: Optional. For webhook tools, contains: - name: Required. The name of the tool. - description: Required. The description of the tool. - url: Required. The URL of the external tool to be called. This URL can be templated like: https://example.com/api/v1/{id}, where {id} is a placeholder for a value that will be provided by the assistant if path_parameters are provided with the id attribute. - method: Optional. The HTTP method to be used. Possible values: [GET, POST, PUT, DELETE, PATCH]. Default value: POST. - headers: Optional. Array of header objects with: - name: String name of the header. - value: String value of the header. Supports mustache templating, e.g., Bearer {{#integration_secret}}test-secret{{/integration_secret}}. Secrets can be found in list_integration_secrets - body_parameters: Optional. JSON Schema object describing the body parameters: - properties: Object defining the properties of the body parameters. - required: Array of strings listing required properties. - type: String. Possible value: "object". - path_parameters: Optional. JSON Schema object describing the path parameters: - properties: Object defining the properties of the path parameters. - required: Array of strings listing required properties. - type: String. Possible value: "object". - query_parameters: Optional. JSON Schema object describing the query parameters: - properties: Object defining the properties of the query parameters. - required: Array of strings listing required properties. - type: String. Possible value: "object". - hangup: Optional. For hangup tools, contains: - description: Optional. Description of the hangup function. - send_dtmf: Optional. For DTMF tools, contains an empty object. This tool allows sending DTMF tones during a call. - transfer: Optional. For transfer tools, contains: - targets: Required. Array of transfer targets, each with: - name: Optional. Name of the target. - to: Required. Destination number or SIP URI. - from: Required. Number or SIP URI placing the call. - custom_headers: Optional. Array of custom SIP headers, each with: - name: Required. Name of the header. - value: Required. Value of the header. Supports mustache templating. eg: {{#integration_secret}}test-secret{{/integration_secret}} to be used with integration secrets (Available secrets can be found in list_integration_secrets) greeting: Optional. A short welcoming message used by the agent. llm_api_key_ref: Optional. LLM API key reference. This is meant to be used for models provided by external vendors. eg: openai, anthropic, Groq, xai-org. Available secrets can be found in list_integration_secrets transcription: Optional. Transcription settings with: - model: Optional. Model to use for transcription. telephony_settings: Optional. Telephony settings with: - default_texml_app_id: Optional. Default TeXML application ID. messaging_settings: Optional. Messaging settings with: - default_messaging_profile_id: Optional. Default messaging profile ID. - delivery_status_webhook_url: Optional. Webhook URL for delivery status updates. insight_settings: Optional. Insight settings with: - insight_group_id: Optional. Insight group ID. dynamic_variables_webhook_url: Optional. Dynamic variables webhook URL. dynamic_variables: Optional. Dynamic variables dictionary.

Returns: Dict[str, Any]: Response data

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYes
assistant_idYes
Behavior2/5

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

With no annotations provided, the description must fully disclose behavioral traits. It does not mention whether updates are merged or replaced, what happens to existing fields, whether it is destructive, or any permission/authentication requirements. For a mutation tool, this is a significant transparency gap.

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 long but well-structured with an Args section and nested bullet points. It is front-loaded with the purpose. Some redundancy exists (e.g., mention of integration secrets appears twice), but the length is largely justified given the generic schema and complex nested parameters.

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?

Given the tool's complexity (many nested objects) and the absence of an output schema, the description is quite complete for input understanding. It covers all documented parameters. However, it lacks information about side effects, return value semantics (only vague 'Response data'), and error conditions, so it falls short of full completeness.

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?

The input schema is minimal (assistant_id and a generic request object with additionalProperties). The description provides exhaustive documentation of all possible fields within request, including nested structures, allowed values, defaults (e.g., HTTP method default POST), and templating examples. This is essential for correct invocation and goes far 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 tool's purpose with a specific verb and resource: 'Update an AI Assistant.' It also distinguishes itself from the sibling create_assistant by focusing on updating an existing agent. The opening sentence is unambiguous.

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 gives clear context for when to use the tool: 'Once there is an agent created, you can talk the user about what can be updated in an easy manner.' This implies it is for existing assistants, but it does not explicitly mention alternatives or exclusions (e.g., use create_assistant for new ones). So it provides context but no explicit when-not-to-use guidance.

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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