memdata-mcp

MCP server for MemData - persistent memory for AI agents.

Give Claude, Cursor, or any MCP-compatible AI long-term memory across conversations.

What it does: Store notes, decisions, and context → retrieve them semantically later. Your AI remembers everything.

Why MemData?

AI assistants forget everything between sessions. MemData fixes that:

• Ingest → Drop in meeting notes, docs, decisions
• Chunk & Embed → Automatically split and vectorize
• Query → Ask questions, get relevant context back
• Tag → AI auto-tags content for better retrieval

Works with Claude Desktop, Claude Code, Cursor, and any MCP client.

Quick Start

1. Get your API key

Create an API key at memdata.ai/dashboard/api-keys

2. Add to your MCP config

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "memdata": {
      "command": "npx",
      "args": ["memdata-mcp"],
      "env": {
        "MEMDATA_API_KEY": "md_your_key"
      }
    }
  }
}

Claude Code (~/.claude.json or project .mcp.json):

{
  "mcpServers": {
    "memdata": {
      "command": "npx",
      "args": ["memdata-mcp"],
      "env": {
        "MEMDATA_API_KEY": "md_your_key"
      }
    }
  }
}

Cursor (~/.cursor/mcp.json):

{
  "mcpServers": {
    "memdata": {
      "command": "npx",
      "args": ["memdata-mcp"],
      "env": {
        "MEMDATA_API_KEY": "md_your_key"
      }
    }
  }
}

3. Restart your client

You should now see MemData tools available.

Tools

Core Tools

Identity & Session Tools (v1.2.0+)

v1.5.0 - Session Start Rename

• memdata_whoami → memdata_session_start - Renamed for clarity. The name now signals "call this first at every session". Description includes 🚀 emoji to catch attention in tool lists.

v1.4.0 UX Improvements

• Visual match quality - Query results show 🟢🟡🟠🔴 indicators for match strength
• Smarter session_start - Prompts to set identity on first use, deduplicates recent activity
• Better ingest feedback - Shows chunk count and explains async AI tagging
• Session continuity - Emphasizes "Continue Working On" and reminds to use session_end

memdata_ingest

Store text in long-term memory.

"Remember that we decided to use PostgreSQL for the new project."

Parameters:

• content (string) - Text to store
• name (string) - Source identifier (e.g., "meeting-notes-jan-29")

memdata_query

Search memory with natural language.

"What database did we choose?"

Parameters:

• query (string) - Natural language search
• limit (number, optional) - Max results (default: 5)

memdata_list

List all stored memories with chunk counts.

memdata_delete

Delete a memory by artifact ID (get IDs from memdata_list).

memdata_status

Check API connectivity and storage usage.

memdata_session_start

🚀 Call this first at the start of every session. Essential for session continuity.

"Start my session" / "What was I working on?"

Returns: agent name, identity summary, session count, last session handoff, recent activity.

v1.5.0: Renamed from memdata_whoami for clarity - the name signals "call me first".

memdata_set_identity

Set or update your agent identity.

Parameters:

• agent_name (string, optional) - Your name (e.g., "MemBrain")
• identity_summary (string, optional) - Who you are and your purpose

memdata_session_end

Save context before ending a session. Next session will see this handoff.

Parameters:

• summary (string) - What happened this session
• working_on (string, optional) - Current focus
• context (object, optional) - Additional context to preserve

memdata_query_timerange

Search memory within a date range.

"What did I work on last week?"

Parameters:

• query (string) - Natural language search
• since (string, optional) - ISO date (e.g., "2026-01-01")
• until (string, optional) - ISO date (e.g., "2026-01-31")
• limit (number, optional) - Max results

memdata_relationships

Find entities that appear together in your memory.

"Who has John Smith worked with?"

Parameters:

• entity (string) - Name to search for
• type (string, optional) - Filter by type (person, company, project)
• limit (number, optional) - Max relationships

How it works

1. Ingest: Text is chunked, embedded, and stored
2. Query: Your question is matched against stored memories using semantic similarity
3. Results: Returns relevant content with similarity scores

Scores of 30-50% are typical for good matches. Semantic search finds meaning, not keywords.

Environment Variables

What this package does

This is a thin MCP client that calls the MemData API. It does not:

• Store any data locally
• Send data anywhere except memdata.ai
• Collect analytics or telemetry

You can inspect the source code in src/index.ts.

Example Usage

Once configured, just talk to your AI:

You: "Remember that we chose PostgreSQL for the user service"
AI: [calls memdata_ingest] → Stored in memory

... days later ...

You: "What database are we using for users?"
AI: [calls memdata_query] → "PostgreSQL for the user service" (73% match)

Links

• MemData - Main site
• Dashboard - Manage your memory
• API Docs - Full documentation
• GitHub - This repo

Contributing

Issues and PRs welcome! This is the open-source MCP client for the hosted MemData service.

License

MIT