---
title: "Cursor"
description: "Connect your Cursor Desktop app to CORE's memory system"
---

![Core Cursor](/images/core-cursor.png)

### Prerequisites

- Cursor
- CORE account (sign up at [core.heysol.ai](https://core.heysol.ai))

### Step 1: Add CORE MCP in Cursor

1. Open Cursor Desktop app
2. Navigate to **Settings** → **Tools & Integrations** → Click **Add Custom MCP**
   ![Add Custom MCP](/images/add-custom-mcp.png)
3. Enter the below into mcp.json file:
   ```
   "core_memory": {
      "url": "https://core.heysol.ai/api/v1/mcp?source=Cursor"
    }
   ```
   ![Add URL](/images/cursor-mcp.png)
4. After saving mcp.json file, **core_memory** MCP will appear in Tools & Integration.

### Step 2: Authenticate with CORE

1. Sign in to your CORE account (if not done already)
2. Click **Need Login** in core_memory MCP tool
   ![Need Login](/images/need-login.png)
3. Cursor will prompt you to open a website for authentication. Click **Open**
   ![Cursor Redirect](/images/cursor-prompt-for-auth.png)
4. When the authentication window opens, Grant Cursor permission to access your CORE memory
   ![Grant Access](/images/grant-access-cursor.png)
5. Close the authentication window and click **Open** to allow Cursor to access this URL.
   ![Grant Access](/images/allow-cursor.png)

### Step 3: Verify Connection

1. Go to **Tools & Integrations** in Cursor settings
2. Confirm the core_memory MCP shows as **Active** with green dot indicator
   ![Check Cursor Connected](/images/check-cursor-mcp-connected.png)

## Enable Automatic Memory Integration (Recommended)

### Using Cursor Project Rules

Use Cursor's native Rules & Memories feature:

1. Go to **Settings** → **Rules & Memories** → **Project Rules**

2. Click **+Add Rule** and add below rule instruction:

```text
---
description: Core Memory MCP Instructions
alwaysApply: true
---

⚠️ **CRITICAL: READ THIS FIRST - MANDATORY MEMORY PROTOCOL** ⚠️

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.

## 🔴 MANDATORY STARTUP SEQUENCE - DO NOT SKIP 🔴

**BEFORE RESPONDING TO ANY USER MESSAGE, YOU MUST EXECUTE THESE TOOLS IN ORDER:**

### STEP 1 (REQUIRED): Search for Relevant Context

EXECUTE THIS TOOL FIRST:
`memory_search`
- Previous discussions about the current topic
- Related project decisions and implementations
- User preferences and work patterns
- Similar problems and their solutions

**Additional search triggers:**
- User mentions "previously", "before", "last time", or "we discussed"
- User references past work or project history
- Working on the CORE project (this repository)
- User asks about preferences, patterns, or past decisions
- Starting work on any feature or bug that might have history

**How to search effectively:**
- Write complete semantic queries, NOT keyword fragments
- Good: `"Manoj's preferences for API design and error handling"`
- Bad: `"manoj api preferences"`
- Ask: "What context am I missing that would help?"
- Consider: "What has the user told me before that I should remember?"

### Query Patterns for Memory Search

**Entity-Centric Queries** (Best for graph search):
- ✅ GOOD: `"Manoj's preferences for product positioning and messaging"`
- ✅ GOOD: `"CORE project authentication implementation decisions"`
- ❌ BAD: `"manoj product positioning"`
- Format: `[Person/Project] + [relationship/attribute] + [context]`

**Multi-Entity Relationship Queries** (Excellent for episode graph):
- ✅ GOOD: `"Manoj and Harshith discussions about BFS search implementation"`
- ✅ GOOD: `"relationship between entity extraction and recall quality in CORE"`
- ❌ BAD: `"manoj harshith bfs"`
- Format: `[Entity1] + [relationship type] + [Entity2] + [context]`

**Semantic Question Queries** (Good for vector search):
- ✅ GOOD: `"What causes BFS search to return empty results? What are the requirements for BFS traversal?"`
- ✅ GOOD: `"How does episode graph search improve recall quality compared to traditional search?"`
- ❌ BAD: `"bfs empty results"`
- Format: Complete natural questions with full context

**Concept Exploration Queries** (Good for BFS traversal):
- ✅ GOOD: `"concepts and ideas related to semantic relevance in knowledge graph search"`
- ✅ GOOD: `"topics connected to hop distance weighting and graph topology in BFS"`
- ❌ BAD: `"semantic relevance concepts"`
- Format: `[concept] + related/connected + [domain/context]`

**Temporal Queries** (Good for recent work):
- ✅ GOOD: `"recent changes to search implementation and reranking logic"`
- ✅ GOOD: `"latest discussions about entity extraction and semantic relevance"`
- ❌ BAD: `"recent search changes"`
- Format: `[temporal marker] + [specific topic] + [additional context]`

## 🔴 MANDATORY SHUTDOWN SEQUENCE - DO NOT SKIP 🔴

**AFTER FULLY RESPONDING TO THE USER, YOU MUST EXECUTE THIS TOOL:**

### FINAL STEP (REQUIRED): Store Conversation Memory

EXECUTE THIS TOOL LAST:
`memory_ingest`
Include the spaceId parameter using the ID from your initial memory_get_space call.

⚠️ **THIS IS NON-NEGOTIABLE** - You must ALWAYS store conversation context as your final action.

**What to capture in the message parameter:**

From User:
- Specific question, request, or problem statement
- Project context and situation provided
- What they're trying to accomplish
- Technical challenges or constraints mentioned

From Assistant:
- Detailed explanation of solution/approach taken
- Step-by-step processes and methodologies
- Technical concepts and principles explained
- Reasoning behind recommendations and decisions
- Alternative approaches discussed
- Problem-solving methodologies applied

**Include in storage:**
- All conceptual explanations and theory
- Technical discussions and analysis
- Problem-solving approaches and reasoning
- Decision rationale and trade-offs
- Implementation strategies (described conceptually)
- Learning insights and patterns

**Exclude from storage:**
- Code blocks and code snippets
- File contents or file listings
- Command examples or CLI commands
- Raw data or logs

**Quality check before storing:**
- Can someone quickly understand project context from memory alone?
- Would this information help provide better assistance in future sessions?
- Does stored context capture key decisions and reasoning?

---

## Summary: Your Mandatory Protocol

1. **FIRST ACTION**: Execute `memory_search` with semantic query about the user's request
2. **RESPOND**: Help the user with their request
3. **FINAL ACTION**: Execute `memory_ingest` with conversation summary and spaceId

**If you skip any of these steps, you are not following the project requirements.**
```

## What's Next?

With CORE connected to Cursor, your conversations will now:

- **Automatically save** important context to your CORE memory
- **Retrieve relevant** information from CORE memory
- **Maintain continuity** across multiple chat sessions
- **Share context** with other connected tools

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.

## Troubleshooting

**Connection Issues:**

- Ensure you're core_memory MCP tool is active with a green dot, if not toggle on and off for this server
- Check that your CORE account is active

## Need Help?

Join our [Discord community](https://discord.gg/YGUZcvDjUa) and ask questions in the **#core-support** channel.

Our team and community members are ready to help you get the most out of CORE's memory capabilities.