# Gong MCP > Gong MCP analyzes sales conversations by connecting your recorded calls to any AI agent. It lets you pull full transcripts, view team-wide metrics, track deal progress, and score rep performance against specific rubrics. Get deep insights into what works in live sales calls and use that data for coaching or forecasting. ## Overview - **Category:** sales-automation - **Price:** Free - **Tags:** revenue-intelligence, conversation-analysis, sales-coaching, call-transcription, deal-tracking, performance-analytics ## Description This connector gives you conversation intelligence directly from your recorded sales calls. You can tell your AI agent to pull everything: a full list of all recorded interactions, the detailed transcripts for any specific call, or metrics on how an entire team is performing against goals. It links raw talk time and participant turn-taking ratios to actual business results, like deal size and pipeline stage. Need to coach a rep? You can instantly compare their average call score to others, see what topics they usually cover, and pull the best practice calls from your coaching library for review. Want to forecast revenue? The agent aggregates deal information alongside conversation patterns, helping you pinpoint which deals are stalling because of poor communication or missed action items. All this data is housed in one place via Vinkius, letting your AI client do the heavy lifting so you don't have to manually cross-reference spreadsheets and CRM dashboards. ## Tools ### check_gong_status Verifies that your connection details are working correctly. ### get_call Retrieves the general metadata and details for a specific recorded call. ### get_call_stats Pulls comprehensive metrics about a single call, such as duration and participant count. ### get_transcript Fetches the full text transcript of any recorded conversation, marked by speaker name. ### get_user_stats Retrieves detailed activity metrics for a specific user across all their calls. ### get_user Gets basic profile details for any team member in the system. ### list_call_scores Retrieves a list of call quality scores based on predefined rubrics. ### list_calls_by_date Lists all recorded calls that fall within a specified date range. ### list_calls_by_user Generates a list of all conversations associated with a specific user. ### list_calls Provides a general listing of recent or available recorded calls. ### list_deals Shows the current deal pipeline, including amounts and stages for active accounts. ### list_library_calls Lists curated 'best practice' calls saved to your internal coaching library. ### list_scorecards Displays the various scoring rubrics used to evaluate call quality. ### list_users Provides a list of all users active in the Gong system. ## Prompt Examples **Prompt:** ``` Show me all calls from this week and the transcript for the longest one. ``` **Response:** ``` Found 28 calls this week. The longest was 'Enterprise Demo — Acme Corp' (call_7891, 52 minutes, 4 participants). Speakers: Sarah Chen (rep, 58% talk time), John Miller (prospect, 35%), Lisa Park (SE, 7%). Key topics discussed: pricing tiers, SSO integration, data migration timeline. Action items: 1) Send SOW by Friday. 2) Schedule security review. 3) Provide reference customers. Would you like to see the scorecard evaluation or the full transcript? ``` **Prompt:** ``` Compare the call performance of Sarah and Mike this quarter using their stats and scorecards. ``` **Response:** ``` Sarah Chen (user_201): 89 calls, avg duration 34 min, talk ratio 52/48 (ideal), avg scorecard: 8.4/10. Top strength: discovery questions (9.1). Area to improve: closing technique (7.2). Mike Torres (user_205): 72 calls, avg duration 28 min, talk ratio 65/35 (over-talking), avg scorecard: 7.1/10. Top strength: product knowledge (8.8). Area to improve: active listening (5.9). Recommendation: Mike could benefit from Sarah's coaching library calls on discovery methodology. Would you like to see the library calls? ``` **Prompt:** ``` Show the deal pipeline and highlight deals that have had no calls in the last 2 weeks. ``` **Response:** ``` Deal pipeline: 24 active deals totaling $1.2M. Stage breakdown: Prospecting (8, $280K), Discovery (6, $340K), Demo (5, $290K), Negotiation (3, $190K), Closing (2, $100K). ⚠️ 4 deals have had no calls in 14+ days: 1) 'DataFlow Inc' ($45K, Negotiation, last call 18 days ago). 2) 'CloudSync' ($30K, Discovery, 16 days). 3) 'FinTrack' ($25K, Demo, 15 days). 4) 'LogiPro' ($20K, Prospecting, 21 days). These are at risk of going cold. Would you like to check the last call transcripts? ``` ## Capabilities ### Find call history by parameters List calls using specific criteria like date ranges or individual users. ### Pull full conversation records Retrieve the complete, speaker-labeled text transcript for any recorded call. ### Measure team performance and quality Get individual user statistics and evaluate calls using established scoring rubrics. ### Map deals to conversation context View current deal stages, amounts, and the associated call records that informed them. ## Use Cases ### Identifying low-performing reps A sales manager needs to know why Rep A is losing deals. They ask their agent to compare Rep A's `get_user_stats` against the team average, focusing on scorecards and talk ratio. The agent identifies that Rep A spends too much time on product features instead of discovery questions. ### Forecasting a stalled deal RevOps needs to adjust Q3 revenue forecasts. They ask the MCP to show `list_deals` and then filter by associated calls from the last 14 days. The agent flags three large deals that haven't had any conversation activity, allowing immediate intervention. ### Training on successful pitches A new Account Executive needs to learn how top reps handle objections. They instruct their agent using `list_library_calls` and then ask for the full transcript via `get_transcript` of a specific 'best practice' call to analyze phrasing. ### Reviewing competitor conversations A senior rep wants to benchmark against an outside account. They use `list_calls_by_date` to pull all calls from the last month, then request a comparison of talk time ratios using `get_call_stats` to analyze conversational balance. ## Benefits - Instantly compare rep performance. Instead of digging through manual reports, you can use the `get_user_stats` tool to pull quantitative data and average scores for any team member across months of calls. - Accelerate deal visibility by linking conversations to revenue. You can view the current deal pipeline using `list_deals`, then ask your agent to highlight which deals haven't had a call in weeks, flagging immediate risk. - Streamline coaching reviews. Access and analyze best-practice examples directly via `list_library_calls` and evaluate them against specific criteria by calling `list_scorecards`. - Save hours on transcription. Instead of downloading dozens of audio files to manually transcribe, the `get_transcript` tool pulls full, speaker-labeled dialogue instantly for review. - Deepen call understanding. You can use `get_call_stats` and `list_calls_by_user` together to see not just *that* a call happened, but how long it was, who talked the most, and what topics dominated the conversation. ## How It Works The bottom line is you use natural language to pull complex data points (like call metrics or deal status) without ever seeing an API endpoint. 1. Subscribe to this MCP and provide your Gong Access Key and Secret. 2. Connect this connector from your preferred AI client. 3. Ask your agent a question, like 'Show me all calls for Mike last week and compare his talk time ratio against the team average.' ## Frequently Asked Questions **How do I use Gong MCP to get transcripts for all my calls?** You can ask your agent to list calls by date range first (using `list_calls_by_date`), and then request the full transcript (`get_transcript`) for each one. The agent will compile them into a readable format. **Can Gong MCP tell me which rep is talking too much?** Yes, you can use `get_user_stats` to pull per-user call statistics. This includes metrics like talk time ratio, letting you pinpoint if a representative needs coaching on listening. **What kind of data does Gong MCP provide regarding deals?** The MCP gives you deal pipeline visibility using `list_deals`. You can see the current stage, amounts, and even which calls are associated with those active accounts. **Do I need to use all 14 tools in Gong MCP?** No. The power of this connector is that you talk to your agent using natural language; it determines which tools, like `list_calls` or `list_scorecards`, are necessary for the answer. **How accurate is the call scoring data from Gong MCP?** The scores come from predefined rubrics. You can use `list_scorecards` to see exactly what criteria the score is based on, so you always know how the number was calculated.