CURSOR · MCP

Cursor Integration

Q: How do I add persistent memory to Cursor?

Run the Dakera server via Docker, then add the dakera-mcp server block to .cursor/mcp.json in your project root with your Dakera URL and API key. Restart Cursor and the AI will have access to 14 core memory tools (86+ available via profiles) automatically.

Q: Does Dakera work with Cursor?

Yes, via the official dakera-mcp MCP server. Cursor supports MCP natively, so no extensions or code changes are needed — just one config block in your project.

Give your Cursor AI coding assistant persistent memory. Dakera's MCP server connects directly to Cursor — your AI remembers project architecture, coding decisions, debugging context, and team patterns across every session.

Zero code changes. Cursor supports MCP natively. Add one config block and Dakera's 14 core memory tools (86+ available via profiles) are available to the AI.

Quick Start

Get persistent memory working in Cursor in under 2 minutes:

Start Dakera

docker run -d --name dakera \
  -p 3300:3300 \
  -e DAKERA_API_KEY=dk-your-key-here \
  -v dakera-data:/data \
  ghcr.io/dakera-ai/dakera:latest

Configure Cursor MCP

Create or edit .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "dakera": {
      "command": "docker",
      "args": ["run", "--rm", "-i",
        "--network", "host",
        "ghcr.io/dakera-ai/dakera-mcp:latest"],
      "env": {
        "DAKERA_URL": "http://localhost:3300",
        "DAKERA_API_KEY": "dk-your-key-here",
        "DAKERA_AGENT_ID": "cursor-dev",
        "DAKERA_MCP_PROFILE": "core"
      }
    }
  }
}

Start Coding with Memory

Restart Cursor. The AI now has access to persistent memory tools. Try asking:

"Remember that we use the repository pattern for database access in this project"
"What architecture decisions have we made so far?"
"Store this debugging approach for the payment module"

What Cursor Gets

With Dakera connected, Cursor's AI has access to 14 core MCP tools (86+ available via profiles) organized into categories:

Category	Tools	Example Use
Memory	store, recall, search, forget	Remember project patterns and decisions
Sessions	start, end, list, memories	Group context by coding session
Knowledge Graph	entities, traverse, link	Map relationships between code concepts
Namespaces	create, list, configure	Isolate memory per project or team
Search	hybrid, fulltext, vector	Find relevant past context by meaning
Decay	config, stats	Auto-forget outdated information

Use Cases for Coding

Project Architecture Memory

Store architectural decisions, patterns, and conventions. When Cursor starts a new session, it can recall the project's design philosophy without you re-explaining.

// Cursor will automatically use these tools:
// dakera_store: "This project uses hexagonal architecture with ports and adapters"
// dakera_store: "Database migrations use sqlx with versioned SQL files"
// dakera_recall: "What's our authentication approach?"

Cross-Session Debugging Context

When debugging a complex issue over multiple sessions, Dakera preserves the investigation trail — what you tried, what failed, and what clues you found.

Team Knowledge Sharing

Multiple developers can connect to the same Dakera instance with different agent IDs. Cross-agent recall lets the AI access shared team knowledge while maintaining individual context.

Code Pattern Library

Store reusable patterns with importance scoring. Frequently accessed patterns score higher and surface more readily in future recalls.

Configuration Options

Variable	Description	Default
DAKERA_URL	Dakera server address	http://localhost:3300
DAKERA_API_KEY	Authentication key	Required
DAKERA_AGENT_ID	Unique identifier for this agent	"default"
DAKERA_NAMESPACE	Memory namespace isolation	"default"
DAKERA_MCP_PROFILE	Tool profile: core, power, admin, or all	"core"

Profile Selection (DAKERA_MCP_PROFILE)

The DAKERA_MCP_PROFILE environment variable controls which tools Cursor sees. Profiles balance capability vs. context window cost:

Profile	Tools	Tokens	Best For
core	14	~2,964	Most users — store, recall, search, sessions, knowledge graphs
admin	32	~5,975	Operators — adds namespaces, API keys, policies, decay config
power	68	~13,014	Advanced agents — adds entity extraction, graph traversal, agent stats, feedback
all	86	~16,026	Full API surface — vectors, bulk ops, export/import, encryption

# Set in your .cursor/mcp.json env block:
"DAKERA_MCP_PROFILE": "power"    # Options: core, power, admin, all

Tip: Start with core (default). Use dakera_discover_tools and dakera_load_tools to pull in additional tools on demand without switching profiles.

Works with Cursor Pro and Business. Any Cursor plan that supports MCP servers works with Dakera. The memory server runs on your own infrastructure — no additional costs beyond your Cursor subscription.

Next Steps

Full MCP Server documentation — all 14 core tools (86+ available via profiles) explained
Quick Start guide — detailed setup walkthrough
Memory Patterns — common patterns for coding workflows
Claude Desktop integration — same memory, different client

Frequently Asked Questions

How do I add persistent memory to Cursor?

Run the Dakera server via Docker, then add the dakera-mcp server block to .cursor/mcp.json in your project root with your Dakera URL and API key. Restart Cursor and the AI will have access to 14 core memory tools (86+ available via profiles) automatically.

Does Dakera work with Cursor?

Yes, via the official dakera-mcp MCP server. Cursor supports MCP natively, so no extensions or code changes are needed — just one config block in your project.

What does Dakera add to Cursor?

Dakera gives Cursor's AI persistent memory across coding sessions, hybrid BM25 + vector semantic search over past interactions, knowledge graph construction for project architecture and decisions, session tracking, and memory decay. All data stays self-hosted on your infrastructure.