⚡ dakera-mcp

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

Works with Claude, Claude Code, and any MCP-compatible framework.

Part of Dakera AI — the memory engine for AI agents.

The Dakera memory engine scores 87.6% on LoCoMo (1,540 questions, standard eval) — benchmark details

Architecture: 14 core tools + on-demand discovery

Starting every agent session with 60+ tool schemas wastes ~15K tokens before you write a single message. dakera-mcp solves this with hybrid tool exposure:

• 14 tools loaded by default — the 12 highest-frequency memory operations + 2 meta-discovery tools
• On-demand expansion — use dakera_discover_tools and dakera_load_tools to fetch additional tool schemas only when you need them

Default tool set (core profile)

| Tool | Purpose | |---|---| | dakera_store | Store a memory with importance, tags, and type | | dakera_recall | Semantic recall by query text | | dakera_search | Advanced memory search with tag/type filters | | dakera_session_start | Start a session to group related memories | | dakera_session_end | End a session with optional summary | | dakera_batch_recall | Bulk filter-based recall (by tags, importance, time) | | dakera_forget | Delete specific memories by ID | | dakera_hybrid_search | Combined vector + BM25 search | | dakera_fulltext_search | BM25 full-text search | | dakera_knowledge_graph | Build a knowledge graph from a seed memory | | dakera_extract | Extract entities and structure from free-form text | | dakera_batch_forget | Bulk delete by tags, type, or time range | | dakera_discover_tools | Search the full tool catalog by keyword or tier | | dakera_load_tools | Load full schemas for specific tools on demand |

Profiles & token cost

| Profile | Tools | ~Tokens | How to enable | |---|---|---|---| | core | 14 | ~2,964 | Default — always loaded | | admin | 32 | ~5,975 | DAKERA_MCP_PROFILE=admin | | power | 68 | ~13,014 | DAKERA_MCP_PROFILE=power | | all | 86 | ~16,026 | DAKERA_MCP_PROFILE=all |

Accessing additional tools

# In your agent: discover what's available
dakera_discover_tools(tier="power")
→ returns names + descriptions, no schemas loaded

# Load schemas for the tools you want
dakera_load_tools(tools=["dakera_consolidate", "dakera_agent_stats"])
→ returns full inputSchema for each tool

Profile selection

The profile controls which tools appear in tools/list. Three ways to set it:

1. Per-request (in tools/list params):

{"profile": "power"}

2. Environment variable (applies to all requests):

DAKERA_MCP_PROFILE=power

3. Default: core (14 tools, ~2,964 tokens)

Run Dakera

The MCP server connects to a Dakera memory server. You need one running first:

docker run -d \
  --name dakera \
  -p 3300:3300 \
  -e DAKERA_ROOT_API_KEY=dk-mykey \
  ghcr.io/dakera-ai/dakera:latest

For persistent storage (recommended):

curl -sSfL https://raw.githubusercontent.com/Dakera-AI/dakera-deploy/main/docker-compose.yml \
  -o docker-compose.yml
DAKERA_API_KEY=dk-mykey docker compose up -d

curl http://localhost:3300/health  # → {"status":"ok"}

Full deployment guide (Docker Compose, Kubernetes, Helm): dakera-deploy

Install

cargo install dakera-mcp

Or with Docker:

docker pull ghcr.io/dakera-ai/dakera-mcp:latest

Connect

Add to .mcp.json (Claude Code) or claude_desktop_config.json (Claude Desktop):

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

To start with the power profile (exposes 68 tools):

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

Why This Exists

AI agents forget everything when the session ends. Dakera fixes that. This MCP server gives your agent a persistent memory layer with zero infrastructure overhead — point it at a Dakera instance and it works.

The 14-tool default keeps your context window lean. The meta-tools let you expand on demand when you need advanced operations like bulk vector upsert, knowledge graph traversal, or memory federation.

→ dakera.ai for hosted instance
→ Self-host with dakera-deploy

Documentation

→ Full docs
→ MCP reference

Related

| Repo | What it is | |---|---| | dakera-py | Python SDK | | dakera-js | TypeScript SDK | | dakera-cli | CLI | | dakera-deploy | Self-host Dakera |

dakera.ai · Documentation · Request Early Access

Part of the Dakera AI open-source ecosystem. Built with Rust. Self-hosted. Zero dependencies.