gong_get_call_details

Retrieve call analytics, topics, and participant details from Gong
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gong_get_call_details

Retrieves comprehensive call analytics and metadata from Gong. Use this tool to understand call dynamics, topics, and speaker participation before retrieving full transcripts.

Get a high-level overview of call content and structure without downloading the full transcript. Perfect for filtering calls before mind creation or analyzing call patterns.

Prerequisites

Gong Integration Required

This tool requires Gong integration to be configured. See the Gong Setup Guide for configuration instructions.

Parameters

callId

Typestring
RequiredYes
DescriptionThe unique call identifier from gong_list_calls response
Examplecall_abc123

Request

1{
2 "callId": "call_abc123"
3}

Response

1{
2 "callId": "call_abc123",
3 "title": "Discovery Call - Enterprise Prospect",
4 "date": "2024-11-15T14:30:00Z",
5 "duration": 3600,
6 "participants": [
7 {
8 "name": "Mike Chen",
9 "email": "mike.chen@yourcompany.com",
10 "role": "internal",
11 "speakingTime": 1200,
12 "wordCount": 2400,
13 "talkRatio": 0.33
14 },
15 {
16 "name": "Sarah Johnson",
17 "email": "sarah.johnson@prospect.com",
18 "role": "prospect",
19 "speakingTime": 2400,
20 "wordCount": 4800,
21 "talkRatio": 0.67
22 }
23 ],
24 "topics": [
25 {
26 "name": "Onboarding Challenges",
27 "mentions": 12,
28 "duration": 900
29 },
30 {
31 "name": "Pricing Discussion",
32 "mentions": 8,
33 "duration": 600
34 },
35 {
36 "name": "Integration Requirements",
37 "mentions": 5,
38 "duration": 450
39 }
40 ],
41 "structure": {
42 "phases": [
43 {
44 "name": "Introduction",
45 "startTime": "00:00:00",
46 "duration": 300
47 },
48 {
49 "name": "Discovery",
50 "startTime": "00:05:00",
51 "duration": 1800
52 },
53 {
54 "name": "Solution Discussion",
55 "startTime": "00:35:00",
56 "duration": 1200
57 },
58 {
59 "name": "Next Steps",
60 "startTime": "00:55:00",
61 "duration": 300
62 }
63 ]
64 }
65}

Response Properties

PropertyTypeDescription
callIdstringThe call identifier
titlestringCall title from Gong
datestringISO 8601 timestamp of the call
durationnumberTotal call duration in seconds
participantsarrayArray of participant objects with analytics
participants[].namestringParticipant’s full name
participants[].emailstringParticipant’s email address
participants[].rolestring"internal" or "prospect"
participants[].speakingTimenumberTotal speaking time in seconds
participants[].wordCountnumberTotal words spoken
participants[].talkRationumberPercentage of total speaking time (0-1)
topicsarrayTopics discussed during the call
topics[].namestringTopic name identified by Gong
topics[].mentionsnumberNumber of times topic was mentioned
topics[].durationnumberTotal time discussing this topic (seconds)
structureobjectCall structure and phases
structure.phasesarrayChronological phases of the call
structure.phases[].namestringPhase name (e.g., “Discovery”, “Pricing”)
structure.phases[].startTimestringStart timestamp (HH:MM:SS)
structure.phases[].durationnumberPhase duration in seconds

Common Use Cases

Review call topics and participants to decide which calls warrant full transcript retrieval.

Example:

"Get details on my last 10 calls and show me which ones
discussed pricing objections"

Natural language workflow:

  • AI lists recent calls
  • Gets details for each call
  • Filters to those with pricing-related topics
  • Presents filtered list for transcript retrieval

What you get:

  • Topic-based filtering
  • Avoid downloading irrelevant transcripts
  • Focus on high-value conversations
  • Efficient use of API calls

Understand speaker dynamics before creating minds.

Example:

"Show me the talk ratios for all participants in
my enterprise calls this month"

Natural language workflow:

  • Lists enterprise calls
  • Gets details for each
  • Analyzes speaker statistics
  • Identifies participation patterns

What you get:

  • Speaker engagement metrics
  • Talk time distribution
  • Identify dominant vs. quiet participants
  • Select balanced conversations for training

Identify recurring topics and themes across multiple calls.

Example:

"Analyze my discovery calls from Q4 and tell me what
topics came up most frequently"

Natural language workflow:

  • Lists Q4 discovery calls
  • Gets details for each call
  • Aggregates topic mentions
  • Ranks by frequency

What you get:

  • Topic frequency analysis
  • Common pain points
  • Recurring objections
  • Product feature requests

Evaluate call structure and flow before using for training.

Example:

"Find calls where the discovery phase lasted more than
20 minutes and the prospect spoke more than 60% of the time"

Natural language workflow:

  • Lists calls
  • Gets details for each
  • Filters by structure and talk ratio criteria
  • Returns qualifying calls

What you get:

  • High-quality discovery calls
  • Prospect-centric conversations
  • Ideal training data for minds
  • Best practice examples

Error Responses

1{
2 "error": "Not Found",
3 "message": "Call with ID 'call_abc123' does not exist"
4}

Solution: Verify the call ID is correct. Use gong_list_calls to get valid call IDs.

1{
2 "error": "Unauthorized",
3 "message": "Gong authentication failed"
4}

Solution: Verify your Gong credentials are configured correctly.

1{
2 "error": "Forbidden",
3 "message": "You do not have permission to access this call"
4}

Solution: Check your Gong user permissions for this call.

1{
2 "error": "Bad Request",
3 "message": "Call details not yet available"
4}

Solution: The call may still be processing. Wait and retry.

Best Practices

Pre-Filter Before Transcripts
  • Use call details to filter calls before retrieving full transcripts
  • Check topics to ensure relevance
  • Review speaker participation for balance
  • Verify call duration and structure
  • Identify calls discussing specific topics
  • Find recurring themes across calls
  • Select calls for topic-specific mind training
  • Track topic evolution over time
  • Use talk ratios to find balanced conversations
  • Identify calls where prospects were engaged
  • Find calls with specific participants
  • Select diverse speaking styles for training

Integration with Mind Reasoner

Workflow: Topic-Based Mind Creation

1

List Calls

Use gong_list_calls to get recent calls

2

Get Details

Use gong_get_call_details to analyze topics and participants

3

Filter

Select calls discussing relevant topics with engaged prospects

4

Get Transcripts

Retrieve full transcripts only for selected calls

5

Create Minds

Build minds from filtered, high-quality conversations

Natural language example:

"Find all my calls where we discussed enterprise features,
the prospect spoke more than 50% of the time, and create
minds for each engaged prospect"

See Also