Which integrations are available for this server?

Allows running Deno scripts with network and environment permissions, including fetching data from APIs and executing TypeScript code Enables execution of Git commands through shell scripts, with examples showing git status checks and branch operations Enables AI agent functionality with web search using Gemini 2.5 Flash model through Google's GenAI API for information retrieval and analysis Supports executing Node.js scripts for data processing and computation tasks with access to input parameters in JSON format Allows execution of npm commands for building projects and running tests through shell scripts Provides AI agent capabilities with web search using GPT-5 model through the OpenAI API for answering queries and research tasks Enables running Python scripts for data analysis and processing tasks with access to environment variables and JSON output

How do I use any-script-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@any-script-mcp run git_status with verbose mode on" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

any-script-mcp

An MCP server that exposes arbitrary CLI tools and shell scripts as MCP Tools

日本語版

Overview

An MCP server that publishes commands defined in YAML files as MCP Tools. By defining tools in a configuration file, you can execute arbitrary shell scripts from MCP clients.

Installation

npx

Claude Code:

$ claude mcp add any-script \ -s user \ -- npx any-script-mcp

json:

{ "mcpServers": { "any-script": { "command": "npx", "args": ["any-script-mcp"] } } }

Configuration

Create a configuration file at $XDG_CONFIG_HOME/any-script-mcp/config.yaml (typically ~/.config/any-script-mcp/config.yaml).

You can also specify custom configuration file paths using the ANY_SCRIPT_MCP_CONFIG environment variable:

# Single configuration file $ ANY_SCRIPT_MCP_CONFIG=/path/to/custom/config.yaml npx any-script-mcp # Multiple configuration files (Unix/macOS - separated by colon) $ ANY_SCRIPT_MCP_CONFIG=/path/to/custom.yaml:$XDG_CONFIG_HOME/any-script-mcp/config.yaml npx any-script-mcp # Multiple configuration files (Windows - separated by semicolon) $ ANY_SCRIPT_MCP_CONFIG=C:\path\to\custom.yaml;%APPDATA%\any-script-mcp\config.yaml npx any-script-mcp

When multiple configuration files are specified:

All tools from all files are merged into a single collection
If the same tool name appears in multiple files, the first occurrence takes precedence
At least one valid configuration file must be successfully loaded
This is useful for separating common tools from project-specific or personal customizations

Testing Your Configuration

You can test your configuration using the MCP Inspector:

$ npx @modelcontextprotocol/inspector npx any-script-mcp

This will open a web interface where you can see your registered tools and test them interactively.

Example Configuration

# yaml-language-server: $schema=https://raw.githubusercontent.com/izumin5210/any-script-mcp/main/config.schema.json tools: - name: echo description: Echo a message inputs: message: type: string description: Message to echo run: | echo "Received: $INPUTS__MESSAGE" - name: git_status description: Check git status with optional branch inputs: branch-name: type: string description: Branch to check out required: false verbose: type: boolean description: Show verbose output default: false run: | if [ -n "${INPUTS__BRANCH_NAME:-}" ]; then git checkout "$INPUTS__BRANCH_NAME" fi if [ "$INPUTS__VERBOSE" = "true" ]; then git status -v else git status fi # Delegate search to codex CLI. Inspired by https://github.com/yoshiko-pg/o3-search-mcp - name: codex-search description: AI agent with web search for researching latest information, troubleshooting program errors, discussing complex problems and design decisions, exploring advanced library usage, and investigating upgrade paths. Supports natural language queries. inputs: prompt: type: string description: What you want to search, analyze, or discuss with the AI agent run: | codex exec \ --model gpt-5 \ --sandbox workspace-write \ --config "sandbox_workspace_write.network_access=true" \ "$INPUTS__PROMPT" \ --json \ | jq -sr 'map(select(.msg.type == "agent_message") | .msg.message) | last' timeout: 600000 # 10 minutes for complex AI operations - name: build description: Run build process with tests run: | npm run build npm test timeout: 180000 # 3 minutes for build and test

Configuration Format

Tool Definition

Each tool has the following fields:

name: Tool name (alphanumeric, underscore, and hyphen only)
description: Tool description
inputs: Input parameter definitions (object format)
run: Shell script to execute
shell: Shell command to execute the script (optional, default: "bash -e {0}")
timeout: Execution timeout in milliseconds (optional, default: 300000 = 5 minutes)

Input Parameters

Each input parameter has the following fields:

type: Parameter type (string, number, boolean)
description: Parameter description
required: Whether the parameter is required (default: true)
default: Default value (optional)

Input parameters are passed as environment variables to shell scripts in two ways:

Individual Environment Variables

Variable names have the INPUTS__ prefix and are converted to uppercase (hyphens are converted to underscores).

Examples:

message → $INPUTS__MESSAGE
branch-name → $INPUTS__BRANCH_NAME

JSON Format (INPUTS_JSON)

All inputs are also available as a single JSON object in the INPUTS_JSON environment variable. This preserves type information, making it easier to work with non-shell interpreters.

Example usage:

// Node.js const inputs = JSON.parse(process.env.INPUTS_JSON); console.log(inputs.num * 2); // count is a number, not a string

Shell Option

The shell option allows you to specify a custom shell or interpreter for executing scripts. The {0} placeholder is replaced with the path to the temporary script file.

Default: "bash -e {0}"

Examples:

# yaml-language-server: $schema=https://raw.githubusercontent.com/izumin5210/any-script-mcp/main/config.schema.json tools: # Python script - name: python_analysis description: Analyze data with Python shell: "python {0}" inputs: data: type: string description: Data to analyze run: | import os import json data = os.environ['INPUTS__DATA'] # Process data with Python result = {"analysis": f"Processed: {data}"} print(json.dumps(result)) # Deno script - name: deno_fetch description: Fetch data with Deno shell: "deno run --allow-net {0}" inputs: endpoint: type: string description: API endpoint run: | const endpoint = Deno.env.get("INPUTS__ENDPOINT"); const response = await fetch(endpoint); console.log(await response.json()); # Using INPUTS_JSON for type preservation - name: add_2 description: add 2 to a number shell: "node {0}" inputs: num: type: number description: a number to add 2 to run: | const inputs = JSON.parse(process.env.INPUTS_JSON); console.log(inputs.num + 2); // number is a number, not a string

Advanced Examples - AI Agents with Web Search

# yaml-language-server: $schema=https://raw.githubusercontent.com/izumin5210/any-script-mcp/main/config.schema.json tools: - name: gemini-search description: AI agent with web search using Gemini 2.5 Flash shell: "deno run -N -E {0}" inputs: query: type: string description: Query for AI search required: true run: | import { GoogleGenAI } from "npm:@google/genai@^1"; const inputs = JSON.parse(Deno.env.get("INPUTS_JSON")); const ai = new GoogleGenAI({ apiKey: Deno.env.get("GEMINI_API_KEY") }); const res = await ai.models.generateContent({ model: "gemini-2.5-flash", contents: inputs.query, config: { tools: [{ googleSearch: {} }], systemInstruction: "...", }, }); console.log( res.candidates?.[0]?.content?.parts?.map((p) => p.text ?? "").join(""), ); - name: gpt-5-search description: AI agent with web search using GPT-5 shell: "deno run -N -E {0}" inputs: query: type: string description: Query for AI search required: true run: | import OpenAI from "jsr:@openai/openai"; const inputs = JSON.parse(Deno.env.get("INPUTS_JSON")); const client = new OpenAI({ apiKey: Deno.env.get("OPENAI_API_KEY") }); const res = await client.responses.create({ model: "gpt-5", tools: [{ type: "web_search_preview" }], input: inputs.query, instructions: "...", }); console.log(res.output_text);

License

MIT

any-script-mcp

any-script-mcp

Overview

Installation

npx

Configuration

Testing Your Configuration

Example Configuration

Configuration Format

Tool Definition

Input Parameters

Individual Environment Variables

JSON Format (INPUTS_JSON)

Shell Option

Advanced Examples - AI Agents with Web Search

License

Resources

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