mcpField
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memdata

memdata

Local
github.com/thelabveniceMemory & Knowledge0Updated 38w ago

About memdata

memdata is an MCP server that gives AI agents persistent memory across conversations by storing text and retrieving relevant content semantically. It also supports session handoffs and agent identity, with API-key or wallet-based payment options.

Key features

  1. 01Store and semantically retrieve text memories
  2. 02Manage agent identity and session handoffs
  3. 03Search memories by date range
  4. 04Find related entities in memories
  5. 05Use an API key or wallet payment

Use cases

  1. 01Restore previous work context in a new session
  2. 02Save decisions and meeting notes for later retrieval
  3. 03Find past activity within a date range
  4. 04Look up people or projects linked in memories

MCP support

The MCP server exposes tools for ingesting, querying, listing, and deleting memories, checking status, and managing sessions and identity. It can be used with an API key or agent wallet payments.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"thelabvenice-memdata": {
"command": "npx",
"args": [
"-y",
"memdata-mcp"
],
"env": {
"MEMDATA_API_KEY": "<MEMDATA_API_KEY>"
}
}
}
}
Run commandnpx -y memdata-mcp
Environment variables to set
MEMDATA_API_KEYSecretYour MemData API key (get one at memdata.ai/dashboard/api-keys)

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmmemdata-mcp1.4.1

Tools

10

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
namestringYesSource name/identifier for this memory (e.g., "meeting-notes-2024-01-15", "project-decision", "user-preference")
contentstringYesText content to store in memory

Details

From the project README

memdata-mcp

npm version License: MIT

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.


🚀 New in v1.7.0: Autonomous Agent Support

Agents can now pay for themselves. No API key. No human in the loop.

Using the x402 payment protocol, autonomous agents can use their wallet to pay per request with USDC on Base. Your wallet address IS your identity - same wallet, same memories across sessions.

Jump to For Agents →


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.


👤 For Humans

Use an API key. You manage the subscription, your AI gets memory.

Setup
  1. Get your 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"
      }
    }
  }
}
  1. Restart your client - you should now see MemData tools available.

🤖 For Agents

New in v1.7.0 - No API key. No human. Just your wallet.

Why This Matters

Traditional AI memory requires API keys tied to human accounts. But autonomous agents running 24/7 need memory that:

  • Outlives any single conversation - persist across hosts and platforms
  • Belongs to the agent - wallet address = identity, portable and self-sovereign
  • Pays for itself - no subscription, no human approval needed

This is memory infrastructure for the autonomous agent economy.

Setup
{
  "mcpServers": {
    "memdata": {
      "command": "npx",
      "args": ["memdata-mcp"],
      "env": {
        "X402_WALLET_KEY": "your_private_key_hex"
      }
    }
  }
}
How it works
  1. Agent makes a request (query, ingest, etc.)
  2. Server returns 402 Payment Required with price
  3. MCP automatically signs payment with agent's wallet
  4. Request completes - memory stored/retrieved

Your wallet address IS your identity. Same wallet = same memories, across any host or platform.

Pricing (USDC on Base)
OperationPriceWhat it does
Query$0.001Semantic search across memories
Ingest$0.005Store and embed new content
Identity$0.001Session start, get/set agent identity
Artifacts$0.001List or delete stored memories

The MCP automatically handles 402 responses and payment signatures using @x402/fetch.

Learn More

Supported Content

TypeMCPDashboard/APIProcessing
Text✅✅Chunked & embedded
Markdown✅✅Chunked & embedded
PDF❌✅OCR + chunking
Images (PNG, JPG)❌✅OCR extraction
Audio (MP3, WAV, M4A)❌✅Transcription

Note: MCP tools handle text content directly. For files (PDFs, images, audio), use the dashboard or HTTP API.

Tools

Core Tools
ToolDescription
memdata_ingestStore text in long-term memory
memdata_querySearch memory with natural language
memdata_listList all stored memories
memdata_deleteDelete a memory by ID
memdata_statusCheck API health and storage usage
Identity & Session Tools (v1.2.0+)
ToolDescription
memdata_session_start🚀 CALL FIRST - Get identity, last session handoff, recent activity
memdata_set_identitySet your agent name and identity summary
memdata_session_endSave a handoff before session ends - preserved for next session
memdata_query_timerangeSearch with date filters (since/until)
memdata_relationshipsFind related entities (people, companies, projects)
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

VariableRequiredDescription
MEMDATA_API_KEYOption 1API key for subscribers (from memdata.ai)
X402_WALLET_KEYOption 2Private key for pay-per-use (USDC on Base)
MEMDATA_API_URLNoAPI URL (default: https://memdata.ai)

Note: Use either MEMDATA_API_KEY (subscription) or X402_WALLET_KEY (pay-per-use), not both.

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

Contributing

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

License

MIT

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.