awesome-agent-toolkits-mcp-server 0.1.0

Auto-discovering MCP server for agent skills

Skills MCP Server

An MCP (Model Context Protocol) server that exposes skill tools as MCP tools, resources, and prompts. It auto-discovers skills from any domain (devops, security, data, etc.) and makes their tools available to any MCP-compatible agent.

Prerequisites

• Python 3.10+
• uv (for uvx usage)
• Domain-specific CLIs as needed by individual skills (e.g., aws, az, kubectl, helm, terraform)

Installation

Option 1: uvx (Recommended)

No install needed — uvx runs the server directly from PyPI:

{
  "mcpServers": {
    "awesome-agent-toolkits-mcp": {
      "command": "uvx",
      "args": ["awesome-agent-toolkits-mcp-server@latest"]
    }
  }
}

Pass --skills-dir to point at your skills directory:

{
  "mcpServers": {
    "awesome-agent-toolkits-mcp": {
      "command": "uvx",
      "args": ["awesome-agent-toolkits-mcp-server@latest", "--skills-dir", "/path/to/skills"]
    }
  }
}

Option 2: Docker (Local Development)

From the repo root:

docker compose up mcp-server

This builds the server image and mounts ./skills as a read-only volume. Configure your agent to use it with stdio transport.

Option 3: Run from source

cd mcp-server
pip install -r requirements.txt
python server.py

Or with a custom skills directory:

python server.py --skills-dir /path/to/skills

The SKILLS_DIR environment variable is also supported.

The server uses stdio transport (stdin/stdout) — it's designed to be launched by an MCP client.

Configuration Examples

VS Code (GitHub Copilot)

Add to your .vscode/mcp.json or user MCP settings:

{
  "servers": {
    "awesome-agent-toolkits-mcp": {
      "command": "uvx",
      "args": ["awesome-agent-toolkits-mcp-server@latest"],
      "transportType": "stdio"
    }
  }
}

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "awesome-agent-toolkits-mcp": {
      "command": "uvx",
      "args": ["awesome-agent-toolkits-mcp-server@latest"]
    }
  }
}

Available Tools (Examples)

The server auto-discovers tools from all installed skills. Below are examples from the included devops skills:

Tool	Description	Parameters
eks_inventory_addons	Inventory EKS add-ons via AWS API	cluster, region, profile (optional)
aks_inventory_addons	Inventory AKS add-ons via Azure API	cluster, resource_group, subscription (optional)
inventory_helm	List Helm releases in current context	—
eks_scan_terraform	Scan Terraform for EKS resources	root_dir
aks_scan_terraform	Scan Terraform for AKS resources	root_dir
check_prereqs	Validate CLI/auth prerequisites	provider (aws|azure), profile (optional)

Available Resources

URI	Description
skills://eks/compatibility	EKS add-on compatibility matrix
skills://aks/compatibility	AKS add-on compatibility matrix
skills://eks/report-templates	EKS HTML report templates
skills://aks/report-templates	AKS HTML report templates

Adding Tools from New Skills

To expose a new skill's tools via MCP, create a tools/mcp_tools.json file in the skill directory:

{
  "tools": [
    {
      "name": "my_tool_name",
      "script": "my_script.py",
      "description": "What this tool does.",
      "params": [
        { "name": "param_name", "flag": "--param-name", "required": true, "description": "Param description" }
      ]
    }
  ]
}

The MCP server auto-discovers these at startup. Tools are namespaced by category: <category>__<tool_name> (e.g., devops__eks_inventory_addons).

Adding Resources

Any files in references/ or assets/ within a skill directory are automatically exposed as MCP resources with URI pattern:

skills://<category>/<skill-name>/references/<filename>
skills://<category>/<skill-name>/assets/<filename>

Available Prompts

Prompts are auto-discovered from tools/mcp_prompts.json in each skill directory, namespaced as <category>__<prompt_name>. Example prompts from the devops skills:

Prompt	Description	Arguments
devops__analyze_drift	Compare inventory vs Terraform versions	inventory_json, terraform_json
devops__changelog_research	Research changelog between versions	package, from_version, to_version
devops__upgrade_plan	Generate upgrade plan from drift analysis	drift_analysis

Adding Prompts

Create a tools/mcp_prompts.json in your skill directory:

{
  "prompts": [
    {
      "name": "my_prompt",
      "description": "What this prompt produces.",
      "params": [
        { "name": "input_data", "required": true, "description": "Data to analyze" }
      ],
      "template": "Analyze the following:\n\n{input_data}\n\nProduce a summary."
    }
  ]
}