Cursor Conversations MCP Server

Lists Cursor chats with summaries, titles, and metadata ordered by recency. HIGHLY RECOMMENDED: Use projectPath parameter to filter conversations by specific project/codebase - this dramatically improves relevance by finding conversations that actually worked on files in that project. Returns conversation IDs for use with get_conversation tool. WORKFLOW TIP: Start with projectPath filtering for project-specific analysis, then call get_conversation with specific IDs from results. Includes AI-generated summaries by default. Supports date range filtering (YYYY-MM-DD format).

Retrieves the complete content of a specific Cursor conversation including all messages, code blocks, file references, title, and AI summary. WORKFLOW TIP: Use conversation IDs from list_conversations, search_conversations, or analytics breakdowns (files/languages arrays contain conversation IDs). Use summaryOnly=true to get enhanced summary data without full message content when you need to conserve context.

Searches through Cursor chat content using exact text matching (NOT semantic search) to find relevant discussions. WARNING: For project-specific searches, use list_conversations with projectPath instead of this tool! This tool is for searching message content, not project filtering.

WHEN TO USE THIS TOOL:

• Searching for specific technical terms in message content (e.g., "useState", "async/await")
• Finding conversations mentioning specific error messages
• Searching for code patterns or function names

WHEN NOT TO USE THIS TOOL:

• ❌ DON'T use query="project-name" - use list_conversations with projectPath instead
• ❌ DON'T search for project names in message content
• ❌ DON'T use this for project-specific filtering

Search methods (all use exact/literal text matching):

1. Simple text matching: Use query parameter for literal string matching (e.g., "react hooks")
2. Multi-keyword: Use keywords array with keywordOperator for exact matching
3. LIKE patterns: Advanced pattern matching with SQL wildcards (% = any chars, _ = single char)
4. Date range: Filter by message timestamps (YYYY-MM-DD format)

IMPORTANT: When using date filters, call get_system_info first to know today's date.

Examples: likePattern="%useState(%" for function calls, keywords=["typescript","interface"] with AND operator.

Get comprehensive analytics and statistics about Cursor chats including usage patterns, file activity, programming language distribution, and temporal trends. BEST PRACTICE: Use projectPath parameter for project-specific analytics - this analyzes only conversations that worked on files in that project, providing much more relevant insights for understanding coding patterns, file usage, and development activity within a specific codebase. WORKFLOW TIP: Always include "files" and "languages" in breakdowns - these contain conversation IDs in their arrays that you can immediately use with get_conversation tool. Use includeConversationDetails=true when you need the full conversation ID list and basic metadata for follow-up analysis.

Find conversations related to a reference conversation based on shared files, folders, programming languages, similar size, or temporal proximity. Use this to discover related discussions, find conversations about the same codebase/project, identify similar problem-solving sessions, or trace the evolution of ideas across multiple conversations.

Extract specific elements from conversations such as file references, code blocks, programming languages, folder paths, metadata, or conversation structure. Use this to build knowledge bases, analyze code patterns, extract reusable snippets, understand project file usage, or prepare data for further analysis and documentation.

Export chat data in various formats (JSON, CSV, Graph) for external analysis, visualization, or integration with other tools. TIP: Use filters.projectPath to export only project-specific conversations for focused analysis of a particular codebase. Use this to create datasets for machine learning, generate reports for stakeholders, prepare data for visualization tools like Gephi or Tableau, or backup chat data in structured formats.

Get system information and utilities for AI assistants. Provides current date, timezone, and other helpful context that AI assistants may not have access to. Use this when you need reference information for date filtering, time-based queries, or other system context.