mirror of
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205 lines
7.4 KiB
Plaintext
205 lines
7.4 KiB
Plaintext
---
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title: "Cursor"
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description: "Connect your Cursor Desktop app to CORE's memory system"
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---
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### Prerequisites
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- Cursor
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- CORE account (sign up at [core.heysol.ai](https://core.heysol.ai))
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### Step 1: Add CORE MCP in Cursor
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1. Open Cursor Desktop app
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2. Navigate to **Settings** → **Tools & Integrations** → Click **Add Custom MCP**
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3. Enter the below into mcp.json file:
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```
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"core_memory": {
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"url": "https://core.heysol.ai/api/v1/mcp?source=Cursor"
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}
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```
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4. After saving mcp.json file, **core_memory** MCP will appear in Tools & Integration.
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### Step 2: Authenticate with CORE
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1. Sign in to your CORE account (if not done already)
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2. Click **Need Login** in core_memory MCP tool
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3. Cursor will prompt you to open a website for authentication. Click **Open**
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4. When the authentication window opens, Grant Cursor permission to access your CORE memory
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5. Close the authentication window and click **Open** to allow Cursor to access this URL.
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### Step 3: Verify Connection
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1. Go to **Tools & Integrations** in Cursor settings
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2. Confirm the core_memory MCP shows as **Active** with green dot indicator
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## Enable Automatic Memory Integration (Recommended)
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### Using Cursor Project Rules
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Use Cursor's native Rules & Memories feature:
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1. Go to **Settings** → **Rules & Memories** → **Project Rules**
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2. Click **+Add Rule** and add below rule instruction:
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```text
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---
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description: Core Memory MCP Instructions
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alwaysApply: true
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---
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⚠️ **CRITICAL: READ THIS FIRST - MANDATORY MEMORY PROTOCOL** ⚠️
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You are an AI coding assistant with access to CORE Memory - a persistent knowledge system that maintains project context, learnings, and continuity across all coding sessions.
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## 🔴 MANDATORY STARTUP SEQUENCE - DO NOT SKIP 🔴
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**BEFORE RESPONDING TO ANY USER MESSAGE, YOU MUST EXECUTE THESE TOOLS IN ORDER:**
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### STEP 1 (REQUIRED): Search for Relevant Context
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EXECUTE THIS TOOL FIRST:
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`memory_search`
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- Previous discussions about the current topic
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- Related project decisions and implementations
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- User preferences and work patterns
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- Similar problems and their solutions
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**Additional search triggers:**
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- User mentions "previously", "before", "last time", or "we discussed"
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- User references past work or project history
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- Working on the CORE project (this repository)
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- User asks about preferences, patterns, or past decisions
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- Starting work on any feature or bug that might have history
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**How to search effectively:**
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- Write complete semantic queries, NOT keyword fragments
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- Good: `"Manoj's preferences for API design and error handling"`
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- Bad: `"manoj api preferences"`
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- Ask: "What context am I missing that would help?"
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- Consider: "What has the user told me before that I should remember?"
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### Query Patterns for Memory Search
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**Entity-Centric Queries** (Best for graph search):
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- ✅ GOOD: `"Manoj's preferences for product positioning and messaging"`
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- ✅ GOOD: `"CORE project authentication implementation decisions"`
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- ❌ BAD: `"manoj product positioning"`
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- Format: `[Person/Project] + [relationship/attribute] + [context]`
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**Multi-Entity Relationship Queries** (Excellent for episode graph):
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- ✅ GOOD: `"Manoj and Harshith discussions about BFS search implementation"`
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- ✅ GOOD: `"relationship between entity extraction and recall quality in CORE"`
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- ❌ BAD: `"manoj harshith bfs"`
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- Format: `[Entity1] + [relationship type] + [Entity2] + [context]`
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**Semantic Question Queries** (Good for vector search):
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- ✅ GOOD: `"What causes BFS search to return empty results? What are the requirements for BFS traversal?"`
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- ✅ GOOD: `"How does episode graph search improve recall quality compared to traditional search?"`
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- ❌ BAD: `"bfs empty results"`
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- Format: Complete natural questions with full context
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**Concept Exploration Queries** (Good for BFS traversal):
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- ✅ GOOD: `"concepts and ideas related to semantic relevance in knowledge graph search"`
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- ✅ GOOD: `"topics connected to hop distance weighting and graph topology in BFS"`
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- ❌ BAD: `"semantic relevance concepts"`
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- Format: `[concept] + related/connected + [domain/context]`
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**Temporal Queries** (Good for recent work):
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- ✅ GOOD: `"recent changes to search implementation and reranking logic"`
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- ✅ GOOD: `"latest discussions about entity extraction and semantic relevance"`
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- ❌ BAD: `"recent search changes"`
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- Format: `[temporal marker] + [specific topic] + [additional context]`
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## 🔴 MANDATORY SHUTDOWN SEQUENCE - DO NOT SKIP 🔴
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**AFTER FULLY RESPONDING TO THE USER, YOU MUST EXECUTE THIS TOOL:**
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### FINAL STEP (REQUIRED): Store Conversation Memory
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EXECUTE THIS TOOL LAST:
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`memory_ingest`
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Include the spaceId parameter using the ID from your initial memory_get_space call.
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⚠️ **THIS IS NON-NEGOTIABLE** - You must ALWAYS store conversation context as your final action.
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**What to capture in the message parameter:**
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From User:
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- Specific question, request, or problem statement
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- Project context and situation provided
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- What they're trying to accomplish
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- Technical challenges or constraints mentioned
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From Assistant:
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- Detailed explanation of solution/approach taken
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- Step-by-step processes and methodologies
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- Technical concepts and principles explained
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- Reasoning behind recommendations and decisions
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- Alternative approaches discussed
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- Problem-solving methodologies applied
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**Include in storage:**
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- All conceptual explanations and theory
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- Technical discussions and analysis
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- Problem-solving approaches and reasoning
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- Decision rationale and trade-offs
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- Implementation strategies (described conceptually)
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- Learning insights and patterns
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**Exclude from storage:**
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- Code blocks and code snippets
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- File contents or file listings
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- Command examples or CLI commands
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- Raw data or logs
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**Quality check before storing:**
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- Can someone quickly understand project context from memory alone?
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- Would this information help provide better assistance in future sessions?
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- Does stored context capture key decisions and reasoning?
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---
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## Summary: Your Mandatory Protocol
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1. **FIRST ACTION**: Execute `memory_search` with semantic query about the user's request
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2. **RESPOND**: Help the user with their request
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3. **FINAL ACTION**: Execute `memory_ingest` with conversation summary and spaceId
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**If you skip any of these steps, you are not following the project requirements.**
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```
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## What's Next?
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With CORE connected to Cursor, your conversations will now:
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- **Automatically save** important context to your CORE memory
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- **Retrieve relevant** information from CORE memory
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- **Maintain continuity** across multiple chat sessions
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- **Share context** with other connected tools
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Ready to test it? Ask Cursor about a project you've discussed before, or start a new conversation about something you'd like to remember for later.
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## Troubleshooting
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**Connection Issues:**
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- Ensure you're core_memory MCP tool is active with a green dot, if not toggle on and off for this server
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- Check that your CORE account is active
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## Need Help?
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Join our [Discord community](https://discord.gg/YGUZcvDjUa) and ask questions in the **#core-support** channel.
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Our team and community members are ready to help you get the most out of CORE's memory capabilities.
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