seekdb-mcp-server 0.0.1

A Model Context Protocol (MCP) server that enables interaction with seekdb database. This server allows AI assistants to perform vector operations, manage collections, execute SQL queries and leverage AI functions through a controlled interface, making database exploration and analysis safer and more structured.

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seekdb mcp server

A Model Context Protocol (MCP) server that enables interaction with seekdb database. This server allows AI assistants to perform vector operations, manage collections, execute SQL queries and leverage AI functions through a controlled interface, making database exploration and analysis safer and more structured.

📋 Table of Contents

• Features
• Available Tools
• Prerequisites
• Installation
  • Install uv
  • Install seekdb
  • Install seekdb mcp server
• Configuration
• Quickstart
• Advanced Features
  • AI Memory System
• Examples
• Security
• License
• Contributing

✨ Features

• Vector Collection Management: Create, list, query, and manage vector collections
• Advanced Search: Full-text search, vector similarity search, and hybrid search
• AI Functions: Integrate with AI models for embedding, completion, and reranking
• AI Memory System: Persistent vector-based memory for AI assistants
• Data Import/Export: Import CSV files to seekdb and export data to CSV
• SQL Operations: Execute SQL queries on seekdb

🛠️ Available Tools

Vector Collection Tools

Tool	Description
create_collection	Create a new vector collection with configurable dimension and distance metric
list_collections	List all collections in seekdb
has_collection	Check if a collection exists
peek_collection	Preview documents in a collection
add_data_to_collection	Add documents with auto-generated embeddings
update_collection	Update existing documents in a collection
delete_documents	Delete documents by ID or filter conditions
query_collection	Query documents using vector similarity search
delete_collection	Delete a collection and all its data

Search Tools

Tool	Description
full_text_search	Perform full-text search using MATCH...AGAINST syntax
hybrid_search	Combine full-text search and vector search with RRF ranking

AI Model Tools

Tool	Description
create_ai_model	Register an AI model (embedding, completion, or rerank)
create_ai_model_endpoint	Create an endpoint connecting a model to an API service
drop_ai_model	Remove a registered AI model
drop_ai_model_endpoint	Remove an AI model endpoint
ai_complete	Call an LLM for text generation
ai_rerank	Rerank documents by relevance using an AI model
get_registered_ai_models	List all registered AI models
get_ai_model_endpoints	List all AI model endpoints

Memory Tools

Tool	Description
seekdb_memory_query	Semantic search for stored memories
seekdb_memory_insert	Store new memories with metadata
seekdb_memory_delete	Delete memories by ID
seekdb_memory_update	Update existing memories

Data Import/Export Tools

Tool	Description
import_csv_file_to_seekdb	Import CSV data as a table or vector collection
export_csv_file_from_seekdb	Export table or collection data to CSV

Database Tools

Tool	Description
execute_sql	Execute SQL queries on seekdb
get_current_time	Get current time from seekdb database

📋 Prerequisites

seekdb Requirements

seekdb supports two deployment modes:

• Embedded Mode: seekdb runs as a library inside your application

  • Supported OS: Linux (glibc >= 2.28)
  • Supported Python: 3.11 to 3.13 (pyseekdb), CPython 3.8 to 3.14 (pylibseekdb)
  • Supported Architecture: x86_64, aarch64

• Client/Server Mode: Connect to a deployed seekdb

🚀 Installation

Install uv (recommended)

uv is an extremely fast Python package installer and resolver. The uvx command (included with uv) is used to run Python tools.

# On macOS and Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

# Or install via pip
pip install uv

After installation, restart your terminal or run source ~/.bashrc (or equivalent) to ensure uvx is available.

Install seekdb

seekdb can be installed as an embedded library. Use pip to install:

# Install pyseekdb (recommended, includes embedded seekdb)
pip install pyseekdb

# Or use a mirror for faster installation
pip install pyseekdb -i https://pypi.tuna.tsinghua.edu.cn/simple

If prompted that the pip version is too low, upgrade pip first:

pip install --upgrade pip

Install seekdb mcp server

Install from PyPI:

pip install seekdb-mcp-server

Or install from source:

git clone https://github.com/oceanbase/awesome-oceanbase-mcp.git
cd awesome-oceanbase-mcp/src/seekdb_mcp_server
pip install .

⚙️ Configuration

seekdb mcp server uses embedded seekdb by default, which requires no additional configuration. The server automatically initializes the database when started.

🚀 Quickstart

seekdb mcp server supports stdio transport mode only.

Stdio Mode

Add the following content to your MCP client configuration file:

Using uvx (recommended):

{
  "mcpServers": {
    "seekdb": {
      "command": "uvx",
      "args": [
        "seekdb-mcp-server"
      ]
    }
  }
}

Running from source:

{
  "mcpServers": {
    "seekdb": {
      "command": "uv",
      "args": [
        "--directory",
        "path/to/awesome-oceanbase-mcp/src/seekdb_mcp_server",
        "run",
        "seekdb-mcp-server"
      ]
    }
  }
}

🔧 Advanced Features

🧠 AI Memory System

The AI Memory System enables your AI assistant to maintain persistent vector-based memory powered by seekdb's vector capabilities.

The memory system allows your AI to maintain continuous context across conversations, eliminating the need to repeat personal preferences and information. Four intelligent tools work together to create a seamless memory experience:

• seekdb_memory_query - Semantically search and retrieve contextual memories
• seekdb_memory_insert - Automatically capture and store important conversations
• seekdb_memory_delete - Remove outdated or unwanted memories
• seekdb_memory_update - Evolve memories with new information over time

Memory System Features

• ✅ Cross-Session Continuity - No need to reintroduce yourself
• ✅ Intelligent Semantic Search - Understands related concepts and context
• ✅ Personalized Experience - AI truly "knows" your preferences
• ✅ Automatic Capture - Important information saved without manual effort
• ✅ Multilingual Support - Store and query memories in any language

Memory Categories

The memory system intelligently organizes information by category:

• Sports/Fitness: football, basketball, swimming, gym, yoga, running, etc.
• Food/Drinks: coffee, tea, pizza, sushi, vegan preferences, etc.
• Work/Career: job, position, company, skills, projects, etc.
• Personal: birthday, hometown, family, languages, hobbies, etc.
• Technology: programming languages, frameworks, databases, tools, etc.
• Entertainment: movies, music, books, games, genres, etc.

📚 Examples

Collection Operations

User: Create a Collection named "document"

AI: I've successfully created a collection named "document" in seekdb with the following default settings:

• Dimension: 384 (vector size)
• Distance metric: L2 (Euclidean distance)

---

User: Insert two sentences into the document: Machine learning is a subset of artificial intelligence, and vector databases enable semantic search

AI: Done! I've inserted two sentences into the "document" collection:

ID	Document
doc1	Machine learning is a subset of artificial intelligence
doc2	vector databases enable semantic search

---

User: Query documents related to machine learning

AI: Here are the documents related to "machine learning", ranked by relevance:

Rank	ID	Document	Distance
1	doc1	Machine learning is a subset of artificial intelligence	5.44
2	doc2	vector databases enable semantic search	7.83

---

User: Update the document about machine learning to "Python is a popular programming language."

AI: Done! I've updated the document:

ID	Before	After
doc1	Machine learning is a subset of artificial intelligence	Python is a popular programming language.

---

User: Delete the document about vector database

AI: Done! I've deleted the document about vector databases:

ID	Deleted Document
doc2	vector databases enable semantic search

---

User: Query all documents in the document

AI: Here are all the documents in the "document" collection:

ID	Document	Metadata
doc1	Python is a popular programming language.	{}

The collection currently contains 1 document.

---

User: Delete the collection document

AI: Done! The collection "document" has been permanently deleted along with all its data.

Search Tools

User: Create a collection, hybrid_search_test

AI: Done! The collection hybrid_search_test has been created successfully with:

• Dimension: 384
• Distance metric: L2

---

User: Insert the following documents:

• "The weather forecast predicts sunny skies for the weekend."
• "Mount Everest is the highest mountain peak in the world."
• "Ocean waves crash against the rocky shoreline at sunset."
• "Apple is a delicious red fruit that grows on trees in orchards."
• "The new iPhone 17 Pro features an advanced camera system and titanium design."

AI: Done! Successfully added 5 documents to the hybrid_search_test collection:

ID	Document
doc1	The weather forecast predicts sunny skies for the weekend.
doc2	Mount Everest is the highest mountain peak in the world.
doc3	Ocean waves crash against the rocky shoreline at sunset.
doc4	Apple is a delicious red fruit that grows on trees in orchards.
doc5	The new iPhone 17 Pro features an advanced camera system and titanium design.

---

User: Perform a hybrid_search, full-text keyword "apple", semantic search "apple smartphone technology"

AI: Here are the hybrid search results combining full-text keyword "apple" and semantic query "apple smartphone technology":

Rank	ID	Document
1	doc4	Apple is a delicious red fruit that grows on trees in orchards.
2	doc5	The new iPhone 17 Pro features an advanced camera system and titanium design.
3	doc2	Mount Everest is the highest mountain peak in the world.
4	doc1	The weather forecast predicts sunny skies for the weekend.
5	doc3	Ocean waves crash against the rocky shoreline at sunset.

Analysis:

• doc4 ranks first due to the strong full-text match with the literal word "Apple"
• doc5 ranks second because it matches semantically with "smartphone technology" (iPhone is an Apple smartphone), even though it doesn't contain the word "apple" explicitly
• The remaining documents have lower relevance scores as they don't match either search criterion well

The hybrid search successfully combined keyword matching (BM25) with semantic similarity (vector search) using Reciprocal Rank Fusion (RRF) to produce these ranked results.

---

🔒 Security

This MCP server uses embedded seekdb by default, which runs locally within your application.

Security Best Practices

1. Local Data Storage: Embedded seekdb stores data locally, ensuring data privacy
2. No Network Exposure: stdio mode doesn't expose any network endpoints
3. File Permissions: Ensure proper file permissions for the data directory

Security Checklist

• ✅ Data stays local with embedded mode
• ✅ No credentials required for embedded mode
• ✅ No network ports exposed
• ✅ Standard file system security applies

📄 License

Apache License - see LICENSE file for details.

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create your feature branch

   git checkout -b feature/amazing-feature
   

3. Commit your changes

   git commit -m 'Add some amazing feature'
   

4. Push to the branch

   git push origin feature/amazing-feature
   

5. Open a Pull Request