Salesloft MCP Demo Server

Salesloft MCP Demo Server

Enables AI-powered analysis of sales call transcripts through natural language queries, allowing users to search conversations, identify objections, and extract insights from customer calls without manually reviewing recordings.

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Salesloft MCP Demo Server

An MCP (Model Context Protocol) server that exposes sales call transcripts to Claude, enabling AI-powered analysis of customer conversations.

What This Demo Shows

This demo illustrates how AI can transform raw sales call data into actionable intelligence. Instead of manually reviewing hours of call recordings, sales leaders can ask natural language questions and get instant answers.

Example queries you can ask Claude:

  • "What calls do we have available?"
  • "Show me all discovery calls"
  • "What are the common objections we're hearing?"
  • "Find mentions of procurement or budget delays"
  • "Summarize the TeleCom Nexus interview"
  • "Which companies are concerned about migration?"

Quick Start

Prerequisites

  • Python 3.11+
  • uv package manager
  • Claude Code or Claude Desktop with MCP support

Installation

  1. Clone or download this repository:
cd salesloft_mcp_demo
  1. Install dependencies:
uv sync
  1. Verify installation:
uv run salesloft-mcp --help

Configure Claude Code

Add to your Claude Code settings (.claude/settings.local.json):

{
  "mcpServers": {
    "salesloft": {
      "type": "stdio",
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/salesloft_mcp_demo",
        "run",
        "salesloft-mcp"
      ]
    }
  }
}

Replace /path/to/salesloft_mcp_demo with the actual path to this directory.

Run the Demo

  1. Restart Claude Code to load the MCP server
  2. Ask Claude about your call data:
    • "What calls do we have?"
    • "Search for mentions of pricing"
    • "Tell me about the TeleCom Nexus call"

Available Tools

list_calls

List available call transcripts with optional filtering.

Parameters:

  • company (optional): Filter by company name (partial match)
  • deal_stage (optional): Filter by stage (Discovery, Demo, Negotiation)
  • limit (optional): Max results to return (default: 50)

get_call

Get the full transcript content for a specific call.

Parameters:

  • call_id (required): The unique identifier for the call

search_calls

Search for keywords across all transcripts.

Parameters:

  • query (required): Search terms
  • limit (optional): Max excerpts to return (default: 10)

Available Resources

  • transcripts://list - Get all call metadata
  • transcripts://call/{call_id} - Get full transcript content

Sample Transcripts

The demo includes 40+ sample interview transcripts covering:

  • Telecom: Pipeline visibility, 5G budget competition
  • Legal Tech: Law firm adoption, procurement complexity
  • E-commerce: Shopify competition, migration concerns
  • HR Tech: Multi-stakeholder buying committees
  • And more...

Testing

Run the test suite:

uv run pytest tests/ -v

Project Structure

salesloft_mcp_demo/
├── src/salesloft_mcp/
│   ├── server.py           # MCP server with tools/resources
│   ├── transcript_loader.py # File parsing utilities
│   └── search.py           # Search functionality
├── transcripts/            # Call transcript files
├── tests/                  # Test suite
├── docs/                   # Documentation
└── .claude/               # Claude Code configuration

Troubleshooting

Server not appearing in Claude Code

  1. Check that the path in settings.local.json is correct
  2. Ensure uv is in your PATH
  3. Restart Claude Code

No transcripts loading

  1. Verify the transcripts/ directory exists
  2. Check that .md files are present
  3. Run uv run python -c "from salesloft_mcp.transcript_loader import load_all_transcripts; print(len(load_all_transcripts()))" to verify

Import errors

Run uv sync to ensure all dependencies are installed.

Future Enhancements

This MVP demonstrates core value. Future versions could add:

  • Real SalesLoft API integration
  • Sentiment analysis per speaker
  • Objection detection and categorization
  • Deal health scoring based on call signals
  • Competitive intelligence extraction

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