Dakera MCP Server

Decay-weighted vector memory for AI agents — 83 MCP tools: store, recall, search, knowledge graphs.

Remote serverstreamable-httpPython

What is the Dakera MCP server?

Connect Dakera to Claude, Cursor or any other MCP client and it stops being a tab you switch to. Decay-weighted vector memory for AI agents — 83 MCP tools: store, recall, search, knowledge graphs. The dakera mcp server is what makes that connection.

What the server does

MCP server for Dakera AI. Gives any MCP-compatible AI agent persistent, queryable memory — with smart token management built in.

Available tools

The toolset is worth reading before you wire it up, because it tells you what the integration is really for:

  • Cargo — The Cargo tool exposed by this server

Credentials and setup notes

Configuration is passed through the environment: DAKERA_API_URL, DAKERA_API_KEY, DAKERA_MCP_PROFILE, DAKERA_ROOT_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.

Installation

Because this one is hosted, setup is mostly authentication — you point your client at the endpoint and approve access. Nothing runs on your machine, so there is no runtime to keep patched.

Where it fits

This sits in the knowledge and memory group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Dakera's toolset — Cargo — is a fair guide to whether it matches your workflow. It is maintained by Dakera-AI; worth a glance at recent repository activity before you build anything load-bearing on it.

This entry was verified against Dakera's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.

Worth knowing first

  • Your data travels to the provider's service, so the usual questions apply about what you send and what they retain.
  • Missing credentials fail quietly in some clients — if no tools show up, check the environment block first.
  • Keep per-call confirmation enabled while you learn its behaviour; it is the cheapest safeguard you have.

Available tools

ToolWhat it does
CargoThe Cargo tool exposed by this server.

How to install the Dakera MCP server

{
  "mcpServers": {
    "dakera": {
      "command": "dakera-mcp",
      "env": {
        "DAKERA_API_URL": "http://localhost:3300",
        "DAKERA_API_KEY": "your-key"
      }
    }
  }
}

Configuration as documented by the project. Restart the client after saving.

Configuration

VariableDescriptionRequired
DAKERA_API_URLEndpoint or connection string the server talks to.Yes
DAKERA_API_KEYCredential the server authenticates with.Yes
DAKERA_MCP_PROFILEConfiguration value read at startup.Optional
DAKERA_ROOT_API_KEYCredential the server authenticates with.Yes

Example prompts to try

  • Use Dakera to Cargo.

Frequently asked questions

It connects Dakera to MCP-compatible AI assistants such as Claude and Cursor, exposing 1 tool (Cargo) that the assistant can call on your behalf. Instead of copying data back and forth by hand, the assistant works with Dakera directly.