EdgeLake MCP Server

A Model Context Protocol (MCP) server for EdgeLake distributed database, providing AI assistants with access to query and explore distributed data across EdgeLake nodes.

Features

• Resource Discovery: List all databases and tables available on EdgeLake nodes
• Schema Inspection: Retrieve table schemas with column information
• SQL Query Execution: Execute complex SQL queries with:
  • WHERE clauses with AND/OR operators
  • GROUP BY aggregations
  • ORDER BY with ASC/DESC sorting
  • JOINs across tables and databases
  • Extended metadata fields (+ip, +hostname, @table_name, etc.)
  • LIMIT for result pagination
• Multi-threaded Execution: Concurrent request handling for optimal performance
• Stateless Design: No session management required

Architecture

┌────────────────────┐         ┌─────────────────────────┐         ┌────────────────────┐
│                    │         │   EdgeLake MCP Server   │         │                    │
│    MCP Client      │◀───────▶│                         │◀───────▶│   EdgeLake Node    │
│  (Claude, etc.)    │  stdio  │   - Resources (list)    │  HTTP   │   (REST API)       │
│                    │         │   - Resources (read)    │         │                    │
└────────────────────┘         │   - Tools (query)       │         └────────────────────┘
                               │   - Tools (node_status) │
                               └─────────────────────────┘

Installation

Prerequisites

• Python 3.10 or higher
• Access to an EdgeLake node with REST API enabled
• EdgeLake node running on accessible IP:port (default: localhost:32049)

Install Dependencies

pip install -r requirements.txt

Configuration

Configure the server using environment variables:

TODO: Update so that node information can be provided dynamically

Example Configuration

Create a .env file:

EDGELAKE_HOST=192.168.1.106
EDGELAKE_PORT=32049
EDGELAKE_TIMEOUT=30
EDGELAKE_MAX_WORKERS=20
LOG_LEVEL=INFO

Or export environment variables:

export EDGELAKE_HOST=192.168.1.106
export EDGELAKE_PORT=32049

Usage

Running the Server

The MCP server runs as a subprocess using stdio transport:

python server.py

MCP Client Configuration

Add to your MCP client configuration (e.g., Claude Desktop):

macOS/Linux: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "edgelake": {
      "command": "python",
      "args": ["/path/to/edgelake/mcp-server/server.py"],
      "env": {
        "EDGELAKE_HOST": "192.168.1.106",
        "EDGELAKE_PORT": "32049"
      }
    }
  }
}

MCP Protocol Implementation

Resources

resources/list

Lists all available databases and tables.

Response Format:

database://{database_name}                     - Database resource
database://{database_name}/{table_name}        - Table resource

Example:

[
  {
    "uri": "database://my_database",
    "name": "Database: my_database",
    "description": "All tables in database 'my_database'",
    "mimeType": "application/json"
  },
  {
    "uri": "database://my_database/users",
    "name": "my_database.users",
    "description": "Table 'users' in database 'my_database'",
    "mimeType": "application/json"
  }
]

resources/read

Reads a specific resource (table schema).

URI Format: database://{database}/{table}

Example Request:

{
  "method": "resources/read",
  "params": {
    "uri": "database://my_database/users"
  }
}

Example Response:

{
  "contents": [
    {
      "uri": "database://my_database/users",
      "mimeType": "application/json",
      "text": "{\n  \"columns\": [\n    {\"name\": \"id\", \"type\": \"INTEGER\"},\n    {\"name\": \"name\", \"type\": \"VARCHAR\"},\n    {\"name\": \"email\", \"type\": \"VARCHAR\"}\n  ]\n}"
    }
  ]
}

Tools

query

Execute SQL queries against EdgeLake with advanced filtering and aggregation.

Parameters:

Example - Simple Query:

{
  "name": "query",
  "arguments": {
    "database": "iot_data",
    "table": "sensor_readings",
    "where": "temperature > 25",
    "limit": 10
  }
}

Example - Complex Aggregation:

{
  "name": "query",
  "arguments": {
    "database": "iot_data",
    "table": "sensor_readings",
    "select": ["device_id", "AVG(temperature) as avg_temp", "COUNT(*) as count"],
    "where": "timestamp > '2025-01-01'",
    "group_by": ["device_id"],
    "order_by": [
      {"column": "avg_temp", "direction": "DESC"}
    ],
    "limit": 20
  }
}

Example - Cross-Database Join:

{
  "name": "query",
  "arguments": {
    "database": "sales",
    "table": "orders",
    "include_tables": ["inventory.products", "customers"],
    "where": "orders.status = 'completed'",
    "limit": 50
  }
}

Example - Extended Fields:

{
  "name": "query",
  "arguments": {
    "database": "iot_data",
    "table": "events",
    "extend_fields": ["+ip", "+hostname", "@table_name"],
    "limit": 100
  }
}

node_status

Get EdgeLake node status and health information.

Example:

{
  "name": "node_status",
  "arguments": {}
}

list_databases

List all available databases in EdgeLake. Use this to discover what databases are available before querying.

Example:

{
  "name": "list_databases",
  "arguments": {}
}

Response:

{
  "databases": ["new_company", "iot_data", "sales"],
  "count": 3
}

list_tables

List all tables in a specific database. Use this to discover what tables are available in a database before querying.

Parameters:

Example:

{
  "name": "list_tables",
  "arguments": {
    "database": "new_company"
  }
}

Response:

{
  "database": "new_company",
  "tables": ["rand_data", "ping_sensor", "events"],
  "count": 3
}

get_schema

Get the schema (column definitions) for a specific table. Use this to understand what columns are available before querying.

Parameters:

Example:

{
  "name": "get_schema",
  "arguments": {
    "database": "new_company",
    "table": "rand_data"
  }
}

Response:

{
  "columns": [
    {"name": "row_id", "type": "SERIAL"},
    {"name": "insert_timestamp", "type": "TIMESTAMP"},
    {"name": "tsd_name", "type": "CHAR(3)"},
    {"name": "tsd_id", "type": "INT"},
    {"name": "timestamp", "type": "timestamp"},
    {"name": "value", "type": "decimal"}
  ]
}

server_info

Get EdgeLake MCP Server version and configuration information.

Example:

{
  "name": "server_info",
  "arguments": {}
}

Response:

{
  "version": "1.0.6",
  "server_name": "edgelake-mcp-server",
  "configuration": {
    "edgelake_host": "192.168.1.106",
    "edgelake_port": 32349,
    "request_timeout": 20,
    "max_workers": 10,
    "log_level": "INFO"
  }
}

Query Building Rules

WHERE Clause

Add filtering conditions with AND/OR operators:

WHERE is_active = true AND age > 18
WHERE status = 'active' OR status = 'pending'
WHERE (category = 'A' OR category = 'B') AND price > 100

GROUP BY

Group results by columns (required when using aggregations with non-aggregated columns):

SELECT device_id, AVG(temperature) FROM sensors GROUP BY device_id

ORDER BY

Order results by columns with optional direction:

ORDER BY created_at DESC
ORDER BY category ASC, price DESC

Include Tables (JOINs)

Include additional tables using comma-separated syntax. For cross-database tables, use db_name.table_name:

FROM orders, customers, inventory.products

Extended Fields

Add EdgeLake metadata fields using special prefixes:

• +ip - Node IP address
• +overlay_ip - Overlay network IP
• +hostname - Node hostname
• @table_name - Source table name

SELECT +ip, +hostname, @table_name, * FROM events

LIMIT

Limit the number of rows returned:

SELECT * FROM users LIMIT 100

API Examples

Using curl (for testing)

# List resources
echo '{"jsonrpc":"2.0","id":1,"method":"resources/list","params":{}}' | python server.py

# Read table schema
echo '{"jsonrpc":"2.0","id":2,"method":"resources/read","params":{"uri":"database://mydb/users"}}' | python server.py

# Execute query
echo '{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"query","arguments":{"database":"mydb","table":"users","where":"is_active = true","limit":10}}}' | python server.py

# Get node status
echo '{"jsonrpc":"2.0","id":4,"method":"tools/call","params":{"name":"node_status","arguments":{}}}' | python server.py

EdgeLake Commands Reference

The MCP server uses EdgeLake's REST API with these commands:

Logging

Logs are written to:

• File: edgelake_mcp.log (in server directory)
• stderr: Console output for debugging

Set log level via LOG_LEVEL environment variable (DEBUG, INFO, WARNING, ERROR).

Development

Project Structure

mcp-server/
├── server.py              # Main MCP server implementation
├── edgelake_client.py     # Multi-threaded EdgeLake HTTP client
├── query_builder.py       # SQL query construction
├── config.py              # Configuration management
├── requirements.txt       # Python dependencies
├── README.md              # This file
└── Design/                # Design documentation
    ├── mcp_service.md
    └── top-level-diagram.monojson

Running Tests

pytest

Code Style

# Format code
black *.py

# Type checking
mypy *.py

Troubleshooting

Connection Issues

Problem: Cannot connect to EdgeLake node

Error: Request error: Connection refused

Solution:

1. Verify EdgeLake node is running: curl http://{host}:{port}
2. Check firewall settings
3. Verify EDGELAKE_HOST and EDGELAKE_PORT are correct

Empty Database List

Problem: No databases returned from resources/list

Solution:

1. Check EdgeLake node has databases: curl -H "command: get databases" http://{host}:{port}
2. Verify user has permissions to view databases
3. Check EdgeLake logs for errors

Query Timeout

Problem: Query takes too long and times out

Solution:

1. Increase EDGELAKE_TIMEOUT environment variable
2. Add more specific WHERE clauses to reduce result set
3. Use LIMIT to restrict rows returned

License

Mozilla Public License 2.0

Support

For issues and questions:

• EdgeLake Documentation: https://edgelake.github.io
• EdgeLake GitHub: https://github.com/EdgeLake
• MCP Specification: https://modelcontextprotocol.io

Contributing

Contributions are welcome! Please ensure:

1. Code follows PEP 8 style guide
2. All tests pass
3. New features include documentation
4. Type hints are used throughout