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Telnyx MCP Server

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update_assistant

Modify AI assistant configurations including name, model, instructions, tools, and telephony settings to customize its behavior and capabilities.

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
assistant_idYes
requestYes
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 'Update an AI Assistant' which implies a mutation operation, but it doesn't describe permissions needed, whether changes are reversible, rate limits, or error handling. The mention of 'talk the user about what can be updated' adds some conversational context but is vague.

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

Conciseness3/5

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

The description is front-loaded with the purpose, but it includes a lengthy, detailed parameter list that could be better structured. While informative, the parameter section is verbose and might be more efficiently presented, though it serves a clear purpose given the schema's deficiencies.

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 complexity (2 parameters with nested objects, no annotations, no output schema), the description does a good job of covering parameter details. However, it lacks information on return values (only states 'Dict[str, Any]: Response data') and behavioral aspects like error cases or side effects, leaving some gaps.

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 description provides extensive details on parameters beyond the input schema, which has 0% coverage and only lists 'assistant_id' and 'request'. It documents optional fields like 'name', 'model', 'instructions', and nested structures for 'tools', 'greeting', etc., adding significant semantic value that compensates for the schema's lack.

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 verb 'Update' and resource 'AI Assistant', making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'create_assistant' or 'get_assistant' beyond the update action, though the distinction is implied.

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 mentions 'Once there is an agent created, you can talk the user about what can be updated' which implies usage after creation, but it lacks explicit guidance on when to use this tool versus alternatives like 'create_assistant' or 'get_assistant', and provides no exclusions or prerequisites.

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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