dakera-mcp-0.2.0 is not a library.

Dakera MCP Server

MCP (Model Context Protocol) server that gives AI agents persistent memory through the Dakera vector database. Connect Claude Desktop, Cursor, Windsurf, or any MCP-compatible client to store, recall, and search memories across sessions.

Features

Agent Memory -- Store, recall, search, and consolidate semantic memories with importance scoring and tagging
Vector Operations -- Full vector CRUD with similarity search, batch queries, multi-vector search, aggregations, and hybrid ranking
Full-Text Search -- BM25-scored document search with hybrid vector+text mode
Knowledge Graph -- Build relationship graphs from memories, summarize, and deduplicate
Session Management -- Group related memories into sessions with metadata and summaries
Inference -- Upsert and query using natural language text with server-side embedding
Namespace Isolation -- Create and manage isolated vector collections with configurable dimensions and distance metrics

Installation

From source

cargo build --release

The binary is at target/release/dakera-mcp.

Docker

docker build -t dakera-mcp .

docker run -i --rm \
  -e DAKERA_API_URL=http://host.docker.internal:3000 \
  -e DAKERA_API_KEY=your-key \
  dakera-mcp

Configuration

Variable	Description	Default
`DAKERA_API_URL`	Dakera API base URL	`http://localhost:3000`
`DAKERA_API_KEY`	API key for authentication (optional)	none
`RUST_LOG`	Log level (`dakera_mcp=debug`, etc.)	`dakera_mcp=info`

Usage

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "dakera": {
      "command": "/path/to/dakera-mcp",
      "env": {
        "DAKERA_API_URL": "http://localhost:3000",
        "DAKERA_API_KEY": "your-api-key"
      }
    }
  }
}

Claude Code

Add to your .claude/settings.json:

{
  "mcpServers": {
    "dakera": {
      "command": "/path/to/dakera-mcp",
      "env": {
        "DAKERA_API_URL": "http://localhost:3000",
        "DAKERA_API_KEY": "your-api-key"
      }
    }
  }
}

Cursor

Add to your Cursor MCP settings:

{
  "mcpServers": {
    "dakera": {
      "command": "/path/to/dakera-mcp",
      "env": {
        "DAKERA_API_URL": "http://localhost:3000",
        "DAKERA_API_KEY": "your-api-key"
      }
    }
  }
}

Docker with MCP clients

Point the command to Docker instead of a local binary:

{
  "mcpServers": {
    "dakera": {
      "command": "docker",
      "args": ["run", "-i", "--rm",
        "-e", "DAKERA_API_URL=http://host.docker.internal:3000",
        "-e", "DAKERA_API_KEY=your-api-key",
        "dakera-mcp"
      ]
    }
  }
}

Available Tools

Memory (8 tools)

Tool	Description
`dakera_store`	Store a memory with content, type (episodic/semantic/procedural/working), importance score, and tags
`dakera_recall`	Recall memories by semantic similarity to a query
`dakera_search`	Advanced memory search with tag and type filters
`dakera_memory_get`	Retrieve a specific memory by ID
`dakera_memory_update`	Update a memory's content, importance, or tags (re-embeds on content change)
`dakera_memory_importance`	Batch-update importance scores for multiple memories
`dakera_forget`	Delete memories by ID or tag filter
`dakera_consolidate`	Consolidate related memories into a single summary

Sessions (5 tools)

Tool	Description
`dakera_session_start`	Start a new session for an agent with optional metadata
`dakera_session_end`	End a session with an optional summary
`dakera_session_list`	List sessions for an agent, optionally active-only
`dakera_session_get`	Get session details including metadata and summary
`dakera_session_memories`	List all memories associated with a session

Agents (3 tools)

Tool	Description
`dakera_agent_stats`	Get agent statistics: memory count, session count, storage usage, top tags
`dakera_agent_memories`	List all memories for an agent with pagination
`dakera_agent_sessions`	List all sessions for an agent

Knowledge (3 tools)

Tool	Description
`dakera_knowledge_graph`	Build a knowledge graph from a seed memory via embedding similarity
`dakera_knowledge_summarize`	Summarize multiple memories into a consolidated memory
`dakera_knowledge_deduplicate`	Find and optionally merge duplicate memories

Namespaces (4 tools)

Tool	Description
`dakera_namespace_list`	List all namespaces
`dakera_namespace_get`	Get namespace details (vector count, dimensions, index stats)
`dakera_namespace_create`	Create a namespace with dimensions and distance metric (cosine/euclidean/dot)
`dakera_namespace_delete`	Delete a namespace and all its vectors

Vectors (14 tools)

Tool	Description
`dakera_vector_upsert`	Upsert vectors with IDs, float arrays, and optional metadata
`dakera_vector_upsert_columns`	Upsert vectors in column format for efficient batch operations
`dakera_vector_query`	Query by similarity, returning nearest neighbors
`dakera_vector_batch_query`	Run multiple similarity searches in parallel
`dakera_vector_multi_search`	Multi-vector search with positive/negative vectors, MMR diversity, and score thresholds
`dakera_vector_unified_query`	Unified query with flexible ranking (vector ANN, text BM25, attribute ordering, combined)
`dakera_vector_delete`	Delete vectors by ID
`dakera_vector_bulk_update`	Update metadata on vectors matching a filter
`dakera_vector_bulk_delete`	Delete all vectors matching a filter
`dakera_vector_count`	Count vectors in a namespace with optional filter
`dakera_vector_export`	Export vectors with pagination
`dakera_vector_aggregate`	Compute aggregations (Count, Sum, Avg, Min, Max) with optional grouping
`dakera_vector_explain`	Explain a query execution plan with cost estimates and optimization hints
`dakera_vector_warm`	Pre-load vectors into cache for faster queries

Full-Text Search (5 tools)

Tool	Description
`dakera_fulltext_index`	Index documents for full-text search
`dakera_fulltext_search`	Search documents with BM25 scoring
`dakera_fulltext_delete`	Delete documents from the full-text index
`dakera_fulltext_stats`	Get index statistics (document count, unique terms, avg doc length)
`dakera_hybrid_search`	Hybrid search combining vector similarity and BM25 with configurable weighting

Inference (3 tools)

Tool	Description
`dakera_text_query`	Query using natural language text (server-side embedding)
`dakera_upsert_text`	Upsert text documents with automatic embedding generation
`dakera_batch_query_text`	Batch query using multiple text queries with automatic embedding

Architecture

┌─────────────────────┐    stdio (JSON-RPC)    ┌──────────────────┐
│   MCP Client        │◄──────────────────────►│  dakera-mcp      │
│ (Claude, Cursor...) │                         │                  │
└─────────────────────┘                         │  ┌────────────┐  │
                                                │  │ Protocol   │  │
                                                │  │ (JSON-RPC) │  │
                                                │  └─────┬──────┘  │
                                                │        │         │
                                                │  ┌─────▼──────┐  │
                                                │  │  Server     │  │
                                                │  │  (dispatch) │  │
                                                │  └─────┬──────┘  │
                                                │        │         │
                                                │  ┌─────▼──────┐  │     HTTP/REST
                                                │  │   Tools     │  │◄──────────────►  Dakera API
                                                │  │  (45 tools) │  │
                                                │  └────────────┘  │
                                                └──────────────────┘

The server communicates over stdio using the MCP JSON-RPC protocol. Each tool call translates to one or more HTTP requests against the Dakera REST API. No local state is kept -- the Dakera API is the single source of truth.

Related Repositories

Repository	Description
dakera	Core vector database engine (Rust)
dakera-py	Python SDK
dakera-js	TypeScript/JavaScript SDK
dakera-go	Go SDK
dakera-rs	Rust SDK
dakera-cli	Command-line interface
dakera-dashboard	Admin dashboard (Leptos/WASM)
dakera-docs	Documentation
dakera-deploy	Deployment configs and Docker Compose
dakera-cortex	Flagship demo with AI agents

License

MIT -- see LICENSE.

dakera-mcp 0.2.0