For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
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HomeDocsAPI ReferenceMCP ServerChat SDKsRelease Notes
HomeDocsAPI ReferenceMCP ServerChat SDKsRelease Notes
  • Introduction
    • Overview
    • Getting started
    • Authentication
    • Data privacy
    • Built-in prompts
    • FAQ
    • Troubleshooting
  • Tools
    • Overview
    • get_ada_configuration
    • get_ada_metric
    • get_available_filters
    • get_conversations
    • get_conversation
    • search_knowledge
    • search_coaching
    • get_improvement_guide
    • list_entities
    • propose_change
    • get_test_cases
    • get_test_runs
    • get_test_run_quota
    • send_feedback
  • Prompt library
    • Overview
    • Improvement recommendations
    • Quick health checks
    • Create visualizations
    • Diagnose performance issues
    • Identify optimization opportunities
    • Review configuration
    • Search knowledge and coaching
    • Test agent responses
    • Deep-dive analysis
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On this page
  • Example prompts
  • Parameters
  • Supported metric types
  • Response
Tools

get_ada_metric

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Retrieves performance metrics and conversation-level metadata for the AI Agent.

Example prompts

  • “What’s our CSAT this week?”
  • “Show my AR and volume for the last 7 days.”
  • “How is my AR this week compared to last week?”
  • “What’s our containment rate trend for the last 30 days?”

Parameters

ParameterTypeRequiredDescription
metric_typestringYesThe metric to retrieve. See supported values below.
start_datestringYesStart date in YYYY-MM-DD format.
end_datestringYesEnd date in YYYY-MM-DD format.
filtersarrayNoArray of filter objects from get_available_filters. Each filter has type, operator, and value fields.

Supported metric types

metric_type valueDescription
resolution_ratePercentage (0–100) of automatically resolved conversations.
csat_ratePercentage (0–100) customer satisfaction.
conversation_volume_engagedCount of conversations where the customer actively engaged.
conversation_volume_openedCount of conversations where a greeting was presented, including those without active engagement.
containment_ratePercentage (0–100) of conversations not escalated to a human agent.
containment_volumeCount of conversations not escalated to a human agent.
conversation_summariesUp to 250 conversation records with ID, inquiry summary, resolution status, reason, and dashboard link.
avg_handle_timeAverage time in seconds customers spent with the AI Agent for contained conversations.
avg_handle_time_agentAverage time in seconds customers spent with live agents after escalation.

Response

Returns an object with metric_type and value.

When using conversation_summaries, per-conversation metadata includes:

FieldDescription
conversation_idUnique identifier for the conversation.
timestampEarliest engagement time.
customer_inquiry_summaryAI-generated summary of what the customer was asking about.
automated_resolution_status"Resolved" or "Unresolved" by automation.
automated_resolution_reasonExplanation for why the conversation received its resolution classification.
conversation_urlLink to the conversation in the Ada dashboard.