
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
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
- 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
- name: build
description: Run build process with tests
run: |
npm run build
npm test
timeout: 180000
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:
const inputs = JSON.parse(process.env.INPUTS_JSON);
console.log(inputs.num * 2);
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:
tools:
- 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']
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());
- 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
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