EdgeLake MCP Server

EdgeLake MCP Server

Enables AI assistants to query and explore distributed data across EdgeLake nodes through SQL operations, resource discovery, and schema inspection. Supports complex queries with joins, aggregations, and metadata fields across multiple databases and tables.

Category
Visit Server

README

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

Variable Description Default
EDGELAKE_HOST EdgeLake node IP/hostname 127.0.0.1
EDGELAKE_PORT EdgeLake REST API port 32049
EDGELAKE_TIMEOUT HTTP request timeout (seconds) 20
EDGELAKE_MAX_WORKERS Max concurrent threads 10
LOG_LEVEL Logging level (DEBUG, INFO, WARNING, ERROR) INFO

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:

Parameter Type Required Description
database string Yes Database name
table string Yes Table name
select array[string] No Columns to select (default: ["*"])
where string No WHERE clause conditions
group_by array[string] No Columns to group by
order_by array[object] No Sort specifications
include_tables array[string] No Additional tables to JOIN
extend_fields array[string] No Metadata fields to add
limit integer No Max rows to return (default: 100)
format string No Output format: json or table (default: json)

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:

Parameter Type Required Description
database string Yes Database name to list tables from

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:

Parameter Type Required Description
database string Yes Database name
table string Yes Table name

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:

MCP Operation EdgeLake Command
List databases GET / with header command: get databases
List tables GET / with header command: get tables where dbms = {database}
Get schema GET / with header command: get columns where dbms = {database} and table = {table}
Execute query GET / with header command: sql {database} format = {format} "{query}"
Node status GET / with header command: get status

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

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured