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Dakera

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Self-hosted Rust-based MCP server for AI agent memory — persistent, queryable memory with hybrid search, knowledge graphs, built-in embeddings, and 14 core tools (expandable to 86+ with profile-based tiering).

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⚡ 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 88.2% 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 69 ~13,205 DAKERA_MCP_PROFILE=power all 87 ~16,212 DAKERA_MCP_PROFILE=all

Accessing additional tools

Copy & paste — that's it
# 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):

Copy & paste — that's it
{"profile": "power"}

2. Environment variable (applies to all requests):

Copy & paste — that's it
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:

Copy & paste — that's it
docker run -d \
 --name dakera \
 -p 3300:3000 \
 -e DAKERA_ROOT_API_KEY=dk-mykey \
 ghcr.io/dakera-ai/dakera:latest

For persistent storage (recommended):

Copy & paste — that's it
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:3000/health # → {"status":"ok"}

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

Connect

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

Copy & paste — that's it
{
 "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):

Copy & paste — that's it
{
 "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.