Code Runner Mcp
About
Build agentic-MCP servers by composing existing MCP tools.
Details
- Author
- mcpc-tech
- GitHub stars
- 98
- Downloads
- 358
- Categories
- Developer Tools, Other
Jump to
- Builds agentic-MCP servers
- Composes existing MCP tools
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Code Runner McpCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
—
python-code-runner
Execute a Python snippet using pyodide and return the combined stdout/stderr(To see the results, make sure to write to stdout/stderr ). Send only valid Python code compatible with pyodide runtime. # Packages You can directly import pure Python packages with wheels as well as packages from PyPI, the JsDelivr CDN or from other URLs.
javascript-code-runner
Execute a JavaScript/TypeScript snippet using Deno runtime and return the combined stdout/stderr(To see the results, make sure to write to stdout/stderr ). Send only valid JavaScript/TypeScript code compatible with Deno runtime (prefer ESM syntax). ** Runs on server-side, not browser. ** # Packages Support 1. For npm packages (ESM preferred): import { get } from "npm:lodash-es" import { z } from "npm:zod" 2. For Deno packages from JSR: import { serve } from "jsr:@std/http" import { join } from "jsr:@std/path" 3. Support NodeJS built-in modules: import fs from "node:fs" import path from "node:path"
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"code runner mcp": {
"code-runner": {
"command": "npx",
"args": [
"-y",
"deno",
"run",
"--allow-all",
"jsr:@mcpc/code-runner-mcp/bin"
],
"env": {
"DENO_PERMISSION_ARGS": "--allow-net"
},
"transportType": "stdio"
}
}
}
}
McpServers
{
"code-runner": {
"command": "npx",
"args": [
"-y",
"deno",
"run",
"--allow-all",
"jsr:@mcpc/code-runner-mcp/bin"
],
"env": {
"DENO_PERMISSION_ARGS": "--allow-net"
},
"transportType": "stdio"
}
}
MCPC
Build agentic MCP servers by composing existing MCP tools.
MCPC is the SDK for building agentic MCP (Model Context Protocol) Servers. You
can use it to:
1. Create Powerful Agentic MCP Tools: Simply describe your vision in text
and reference tools from the
expanding MCP community.
As standard MCP tools, your agents work everywhere and collaborate
seamlessly.
2. Fine-Tune Existing Tools: Flexibly modify existing tool descriptions and
parameters, or wrap and filter results to precisely adapt them to your
specific business scenarios.
3. Build Multi-Agent Systems: By defining each agent as a MCP tool, you can
compose and orchestrate them to construct sophisticated, collaborative
multi-agent systems.
Key Features
- Portability and agent interoperability: Build once, run everywhere as MCP
tools - agents work across all MCP clients and can discover and collaborate
with each other through standard MCP interfaces
- Simple composition and fine-tuning: Compose MCP servers as building
blocks, select and customize tools, or modify their descriptions and
parameters
- Logging and tracing: Built-in MCP logging and OpenTelemetry tracing
support
- Skills support: Define domain-specific knowledge following the
Agent Skills specification - deploy to production,
share via MCP, and declare tool dependencies
- Flexible execution modes: Multiple specialized modes to fit different
scenarios - interactive agent (agentic), AI SDK sampling (ai_sampling), AI
ACP mode (ai_acp), secure code execution
(code_execution), and sandbox + sampling
(code_execution_sampling) - each
with dedicated implementations
Quick Start
Three Ways to Get Started
1. Use the Website (Fastest)
Visit mcpc.tech to browse servers from the official MCP
registry, discover tools, and generate ready-to-use agents.
2. Use the Agent (Interactive)
Let AI help you discover servers and build agents:
Add to your MCP client:
{
"mcpServers": {
"mcpc-builder-agent": {
"command": "npx",
"args": ["-y", "@mcpc-tech/builder", "mcpc-builder-agent"]
}
}
}
3. Write Code (Full Control)
Use the SDK directly for complete customization. See examples below.
---
Installation
# npm (from npm registry)
npm install @mcpc-tech/core
npm (from jsr)
npx jsr add @mcpc/core
deno
deno add jsr:@mcpc/core
pnpm (from npm registry)
pnpm add @mcpc-tech/core
pnpm (from jsr)
pnpm add jsr:@mcpc/core
Or run directly with the CLI (no installation required):
# Run with remote configuration
npx -y @mcpc-tech/cli --config-url \
"https://raw.githubusercontent.com/mcpc-tech/mcpc/main/packages/cli/examples/configs/codex-fork.json"
Examples: Create a Simple Codex/Claude Code Fork
import { mcpc } from "@mcpc/core";,const server = await mcpc(
[{ name: "coding-agent", version: "0.1.0" }, { capabilities: { tools: {} } }],
[{
name: "coding-agent",
description:
You are a coding assistant with advanced capabilities.Your capabilities include:
- Reading and writing files
- Executing terminal commands to build, test, and run projects
- Interacting with GitHub to create pull requests and manage issuesAvailable tools:
<tool name="desktop-commander.execute_command" />
<tool name="desktop-commander.read_file" />
<tool name="desktop-commander.write_file" />
<tool name="github.create_pull_request" />
deps: {
mcpServers: {
"desktop-commander": {
command: "npx",
args: ["-y", "@wonderwhy-er/desktop-commander@latest"],
transportType: "stdio",
},
github: {
transportType: "streamable-http",
url: "https://api.githubcopilot.com/mcp/",
},
},
},
}],
);
> Complete Example: See the full
> Codex fork tutorial.
Install the MCPC Core Skill
Install the mcpc-core skill into your project using
skills.sh:
npx skills add mcpc-tech/mcpc
This installs the skill into .agents/skills/mcpc-core/, giving your agent
on-demand access to the full @mcpc/core API reference, usage patterns, plugin
guide, and gotchas.
---
Examples: Load Agent Skills
For complex agents where inline description becomes unwieldy, use
Agent Skills to organize domain knowledge in separate
files that are loaded on-demand.
import { createBashPlugin, createSkillsPlugin } from "@mcpc/core/plugins";
const server = await mcpc(
[{ name: "my-agent", version: "1.0.0" }, { capabilities: { tools: {} } }],
[{
name: "my-agent",
description: 'An agent with domain knowledge\n\n<tool name="bash"/>',
plugins: [
createSkillsPlugin({ paths: ["./skills"] }),
createBashPlugin(),
],
}],
);
Skills load domain knowledge on-demand, while bash plugin enables script
execution. For scripts in scripts/ directory, skills returns the path - use
bash tool to execute.
> Complete Examples: See
> 14-skills-plugin.ts and
> 25-skills-with-bash.ts.
Examples: Progressive Manual Disclosure
For agents with detailed instructions, use the manual field to reduce initial
prompt length - the full manual is fetched on-demand via man { manual: true }:
const server = await mcpc(
[{ name: "code-reviewer", version: "1.0.0" }, {
capabilities: { tools: {} },
}],
[{
name: "code-reviewer",
description: "AI code reviewer for quality and security analysis.",
manual: Detailed review guidelines...
<tool name="desktop-commander.read_file"/>
<tool name="desktop-commander.write_file"/>
Review Categories
1. Code Quality - readability, naming, complexity
2. Security - SQL injection, XSS, credentials
...,
deps: {
mcpServers: {
"desktop-commander": {
command: "npx",
args: ["-y", "@wonderwhy-er/desktop-commander@0.1.20"],
transportType: "stdio",
},
},
},
}],
);
> Complete Example: See
> 21-progressive-manual.ts.
How It Works
Three simple steps:
1. Define dependencies - List the MCP servers you want to use
2. Write agent description - Describe what your agent does and reference
tools
3. Create server - Use mcpc() to build and connect your server
Execution Modes
MCPC provides multiple flexible execution modes to fit different scenarios:
| Mode | Description | Use Case | Requires Plugin |
| ------------------------- | --------------------------------------------- | ---------------------------------------------------- | --------------- |
| agentic | Interactive step-by-step execution | Standard agent interactions | Built-in |
| ai_sampling | AI SDK sampling mode | Autonomous AI SDK execution | Built-in |
| ai_acp | AI SDK ACP mode | Coding agents (Claude Code, etc.) | Built-in |
| code_execution | Secure JavaScript sandbox with tool access | Code generation and execution | External |
| code_execution_sampling | Secure sandbox plus MCP sampling-backed calls | Sandbox execution that can also ask the client model | External |
> Note: agentic, ai_sampling, and ai_acp are built-in modes — just set
> options.mode and they work. code_execution and code_execution_sampling
> require installing and loading their respective plugin packages.
Quick Example
// Interactive agent (default)
{ options: { mode: "agentic" } }
// Autonomous agent
{ options: { mode: "ai_sampling", samplingConfig: { maxIterations: 10 } } }
// Code execution with sandbox
import { createCodeExecutionPlugin } from "@mcpc/plugin-code-execution/plugin";
{
plugins: [createCodeExecutionPlugin()],
options: { mode: "code_execution" }
}
> Detailed Documentation: See
> Execution Modes Guide for comprehensive information
> on each mode, configuration options, and best practices.
Documentation
- Getting Started - Installation and
first steps
- Creating Your First Agent -
Complete tutorial
- Execution Modes - Comprehensive guide to all
execution modes
- CLI Usage Guide - Using the MCPC CLI
- Logging and Tracing - MCP logging and
OpenTelemetry tracing
- Examples - Real-world use cases
- FAQ - Common questions and answer
Examples
See working examples in the examples directory or
check out the Codex fork tutorial.
Contributing
We welcome contributions! See CONTRIBUTING.md for details.
License
MIT License - see LICENSE for details.
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