Gcore MCP Server

MCP (Model Context Protocol) server for Gcore API. This server provides tools for interacting with Gcore Cloud API via LLM assistants.

Usage

Note: As we have multiple resources available, providing all of them at once to most LLM clients can overwhelm the model and lead to confusion among the tools. For most clients, it is recommended to specify only the necessary resources for your task to ensure optimal performance and clarity. Some clients, like Claude Code, handle this differently — see below.

Integration with Cursor IDE

Add the server to your Cursor IDE configuration file (~/.cursor/mcp.json):

{
  "mcpServers": {
    "gcore-mcp-server": {
      "command": "uvx",
      "args": ["--from", "gcore-mcp-server@git+https://github.com/G-Core/gcore-mcp-server.git", "gcore-mcp-server"],
      "env": {
        "GCORE_API_KEY": "4***1",
        "GCORE_TOOLS": "instances,management,cloud.gpu_baremetal.clusters.*"
      }
    }
  }
}

Integration with Claude Code

Add the server to your Claude Code configuration file (~/.claude.json):

{
  "mcpServers": {
    "gcore-mcp-server": {
      "command": "uvx",
      "args": ["--from", "gcore-mcp-server@git+https://github.com/G-Core/gcore-mcp-server.git", "gcore-mcp-server"],
      "env": {
        "GCORE_API_KEY": "4***1",
        "GCORE_TOOLS": "*"
      }
    }
  }
}

Setting GCORE_TOOLS=* loads all available tools, which works well with Claude Code thanks to its Tool Search feature. Unlike other clients, Claude Code defers tool schema loading — tool schemas are only fetched on demand when they match a query. This means registering many tools doesn't bloat the context window.

Claude Code shows a warning when tools exceed 10% of the context window. With deferred loading, this isn't an issue even with all tools enabled.

The uvx command runs the server in a temporary environment without requiring a persistent installation. See Running in a Temporary Environment for more details.

Note: You can find instructions on how to obtain a Gcore API Key here.

Optional variables:

• GCORE_BASE_URL: "https://api.gcore.com",
• GCORE_CLOUD_PROJECT_ID: "1",
• GCORE_CLOUD_REGION_ID: "76",
• GCORE_CLIENT_ID: "2",

Configuration

Tool Selection

The server uses a unified configuration approach via the GCORE_TOOLS environment variable. This single variable can contain a mix of predefined toolset names and custom patterns:

# Mixed toolsets and patterns
export GCORE_TOOLS="instances,management,cloud.gpu_baremetal.clusters.*,dns.records.create"

# Only toolsets
export GCORE_TOOLS="instances,management"

# Only patterns  
export GCORE_TOOLS="cloud.*,waap.*"

# Default behavior (if not set)
# Uses "management,instances" toolsets for HTTP mode, "management" for stdio

Configuration Modes

1. Default Mode (no configuration)

   • HTTP transport: Uses management,instances toolsets
   • stdio transport: Uses management toolset

2. Toolset Mode (predefined tool collections)

   • Use predefined toolset names: instances, management, ai_ml, etc.
   • Example: GCORE_TOOLS="instances,management"

3. Pattern Mode (custom tool filtering)

   • Use wildcard patterns to match tool names from the Gcore SDK
   • Exact matches: cloud.instances.create, dns.records.delete
   • Wildcard matches: cloud.*, waap.*, cloud.gpu_baremetal.clusters.*
   • Example: GCORE_TOOLS="cloud.instances.*,waap.*"

4. Combined Mode (toolsets + patterns)

   • Mix predefined toolsets with custom patterns
   • Toolset definitions have priority over pat

... View full README on GitHub