Dakera

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 withhybrid tool exposure:

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

# Global install npm install -g @dakera-ai/dakera-mcp # Or run directly without installing npx @dakera-ai/dakera-mcp

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

Add to.mcp.json(Claude Code) orclaude_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" } } } }

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.

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

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