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Search and analyze earnings call transcripts with speaker-level segments.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "EarningsCalls.dev" MCP server from askskill: Run: claude mcp add 'io-github-stockmarketscan-earningscalls-mcp' -- npx -y @earningscalls/mcp-server
Please find the most recent earnings call transcript for a company and show the key content grouped by speaker segments.
Returns the relevant earnings call text and organizes key points by speaker, such as executives or analysts.
Search earnings call transcripts for mentions of "AI investment" or similar phrases, and list the companies with surrounding context.
Returns matching call excerpts, the associated companies, and surrounding context for further analysis.
Please extract the management speaker sections from a target earnings call, ignore the Q&A part, and summarize the main points.
Outputs management-related transcript sections and a brief summary for quick understanding of the company's key messages.
Researchers or analysts can use it to search earnings call transcripts and quickly locate how companies discuss performance, strategy, or market conditions. Speaker segmentation also helps separate management and analyst viewpoints.
Developers can connect this MCP tool to AI workflows so models can search, extract, and summarize earnings call text. It fits applications for investment research or corporate intelligence.
Product managers or data analysts can run full-text searches around a topic and compare how different companies discuss similar issues on earnings calls. This helps surface shared trends and differences faster.
It provides more than 177,000 earnings call transcripts, with speaker segments and full-text search. It is designed for AI to search and analyze the content directly.
Yes. The description explicitly mentions speaker segments, meaning the tool can organize earnings call content by speaker.
The provided material does not include installation steps, runtime details, or key requirements. Please see the source repository for specifics.
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