MCP Server Bash SDK

MCP Server Bash SDK

The MCP Server Bash SDK is a lightweight and minimal implementation of an MCP server written purely in Bash, enabling seamless integration of shell scripts with the Model Context Protocol for exposing tools and workflows to LLM-powered AI systems.

Author: muthuishere


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What is MCP Server Bash SDK?

MCP Server Bash SDK is a simple, fast, and zero-overhead server implementation of Model Context Protocol, built entirely in Bash shell. It allows you to expose custom shell functions as MCP tools, making them available for invocation by AI agents and LLM assistants through the standard MCP protocol. With support for dynamic tool discovery, external JSON configuration, and full JSON-RPC 2.0 compatibility over stdio, it is ideal for integrating local scripts and workflows into your AI infrastructure without the complexity and resource requirements of Node.js or Python servers.

How to Configure

  1. Ensure Bash and jq are installed on your system.
  2. Clone the repository and enter the directory: 
    git clone https://github.com/muthuishere/mcp-server-bash-sdk
cd mcp-server-bash-sdk
  3. Make your server scripts executable: 
    chmod +x mcpserver_core.sh your_server.sh
  4. Define your business logic in a custom Bash script (e.g., weatherserver.sh) using Bash functions named with the tool_ prefix.
  5. Describe available tools in a JSON file (tools_list.json) under the assets/ directory.
  6. Configure server metadata and capabilities in mcpserverconfig.json.
  7. (Optional) Set environment variables as needed, e.g., for API keys.

How to Use

  1. Start your MCP server script (e.g., ./weatherserver.sh). The server listens for JSON-RPC 2.0 messages on stdio.
  2. From an MCP-compatible host (such as an LLM agent or AI assistant), invoke tools via the standardized tools/call endpoint by sending appropriate JSON-RPC requests, for example: 
    echo '{"jsonrpc": "2.0", "method": "tools/call", "params": {"name": "get_weather", "arguments": {"location": "New York"}}, "id": 1}' | ./weatherserver.sh
  3. Integrate with VS Code or Copilot by pointing the "mcp.servers" config to your server script's path, passing environment variables as needed.
  4. For custom tool logic, simply add new tool_* shell functions, document them in your tools JSON file, and restart the server.

Key Features

  • Pure Bash implementation: No need for Node.js or Python runtimes.
  • Full JSON-RPC 2.0 and MCP protocol support over stdio.
  • Easy to extend: Add new tools by defining functions with the tool_ prefix.
  • Dynamic tool discovery through a JSON file (tools_list.json).
  • Supports external configuration with JSON files for server info, capabilities, and instructions.
  • Works well with local files, data, and APIs using familiar shell scripting.
  • Plug and play with major AI toolchains, including VS Code and GitHub Copilot's Chat.

Use Cases

  • Extend LLM agents with direct, secure access to system commands or local business logic without writing additional servers or wrappers.
  • Expose bash-native tooling, scripts, or workflows for automation, monitoring, or DevOps tasks to AI copilots and chatbots.
  • Quickly prototype MCP servers for operations like weather querying, movie info retrieval, or any workflow based on CLI and shell utilities.
  • Enable AI assistants to interact with local file systems, perform dynamic tasks, or call external APIs securely via command line tools.

FAQ

Q1: Do I need to know Bash scripting to use this SDK?
A1: Yes, basic knowledge of Bash scripting is required to define tool functions and handle logic, but the structure is simple and follows standard conventions.

Q2: Is this suitable for production and high-traffic applications?
A2: The SDK is best for small- to medium-scale automation, local environments, and prototyping. For production-grade, concurrent, or high-throughput applications, more robust implementations (e.g., in Go or Python) are recommended.

Q3: Can I use the server with any LLM provider?
A3: Yes, MCP standardizes communication, so you can switch LLM backends or use any MCP-compatible AI host without server code changes.

Q4: How do I add a new tool or function?
A4: Simply define a new tool_<name>() function in your server script, specify its schema/details in tools_list.json, and restart the server.

Q5: What are the main limitations?
A5: No support for concurrent processing, limited memory management, no streamed responses, and it's not designed for high traffic; it's focused on simplicity and local use.