What can you do with this server?

This server lets an AI assistant execute code remotely on HackerEarth's sandboxed cloud servers and read back the real output. * Run code in any of 24 programming languages via the run_code tool (requires code and language), returning actual printed output and error messages. * Test and verify code — get real execution results instead of the assistant guessing whether a program works (e.g., compile/runtime errors, output values). * Discover supported languages with list_languages, which returns the exact identifiers (e.g., PYTHON3, JAVASCRIPT_NODE) to pass to run_code. * Use friendly aliases such as python, which are normalized to the correct HackerEarth identifier. * Run untrusted code safely — nothing executes on your local machine and no toolchains need installing. * Connect from any MCP-compatible assistant (Claude Desktop, Claude Code, Cursor, etc.), needing only a free HACKEREARTH_KEY, with graceful handling of a missing key or unknown language.

Which integrations are available for this server?

Provides tools to run code in 24 programming languages through HackerEarth's sandboxed code execution API, returning printed output and compile/runtime errors, and to list supported language identifiers.

How do I use hackerearth-mcp?

1. Click on "Deploy 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., "@hackerearth-mcp Run this Python code and show the output: print('Hello, world!')" 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.

hackerearth-mcp

by pratham-jain33

Overview Schema Source Related Servers Discussions Admin

Python

Remote

hackerearth-mcp

mcp-name: io.github.pratham-jain33/hackerearth-mcp

License: MIT Python 3.10+ MCP Compatible Version PyPI Stars Tests

Also listed on Glama with one-click deploy.

Give any AI assistant a code run button.

Unofficial community project. Not made by, endorsed by, or affiliated with HackerEarth. You bring your own free HackerEarth API key.

Why

AI assistants are great at writing code and terrible at knowing whether it works. They guess. This server plugs into any MCP-compatible assistant (Claude Desktop, Claude Code, Cursor, and more) and lets it actually run code in 24 programming languages on HackerEarth's sandboxed servers, then read the real output. No local toolchains to install. Nothing untrusted ever touches your machine.

Related MCP server: Netmind Code Interpreter

Tools

Tool	Description
`run_code`	Sends code plus a language to HackerEarth, waits for it to run, and returns the printed output and any error messages.
`list_languages`	Lists every language HackerEarth can run, with the exact identifiers to pass to `run_code`. Takes no inputs.

Installation

# Quick install via uvx (recommended)
uvx hackerearth-mcp

# Or install with pip
pip install hackerearth-mcp

# Install from source
git clone https://github.com/pratham-jain33/hackerearth-mcp
cd hackerearth-mcp
python -m venv .venv
.venv/bin/pip install -e .

Features

Exposes two MCP tools: run_code and list_languages.
Executes code in 24 programming languages via HackerEarth's sandboxed cloud servers.
Provides friendly language aliases (e.g., python → PYTHON3).
Returns real program output and error messages.
Gracefully handles missing HACKEREARTH_KEY and unknown languages without making network calls.
Lists all supported language identifiers.
Simple installation via uvx or pip.
Compatible with any MCP‑compatible AI assistant (Claude Desktop, Cursor, etc.).

Tech/framework used

Language: Python (>=3.10)
Core libraries: FastMCP (provides MCP server framework), requests (HTTP client)

Quickstart

1. Get a free HackerEarth API key

Register a client in the HackerEarth developer dashboard to receive a client-secret. The free tier includes a generous request quota.

2. Install

No cloning needed:

uvx hackerearth-mcp

or with pip:

pip install hackerearth-mcp

From source instead:

git clone https://github.com/pratham-jain33/hackerearth-mcp
cd hackerearth-mcp
python -m venv .venv
.venv/bin/pip install -e .

3. Connect your assistant

Claude Desktop config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "hackerearth": {
      "command": "uvx",
      "args": ["hackerearth-mcp"],
      "env": {
        "HACKEREARTH_KEY": "paste-your-client-secret-here"
      }
    }
  }
}

From source, point at your checkout instead:

{
  "mcpServers": {
    "hackerearth": {
      "command": "/absolute/path/to/hackerearth-mcp/.venv/bin/python",
      "args": ["/absolute/path/to/hackerearth-mcp/server.py"],
      "env": {
        "HACKEREARTH_KEY": "paste-your-client-secret-here"
      }
    }
  }
}

Restart Claude Desktop, then try: "run this Python program and tell me what it prints: print(6 * 7)"

Configuration

Variable	Required	Description
`HACKEREARTH_KEY`	Yes	Your HackerEarth `client-secret`. Read from the environment, never hardcoded, never sent to the assistant in chat.

How it works

(This section is written by the project's author.)

You ask the assistant to run some code.
The assistant calls run_code with the code and the language.
The server sends it to HackerEarth with your key and gets back a tracking token.
The server polls HackerEarth until the run finishes.
HackerEarth runs the code in a sealed sandbox on their machines.
The server hands the output back to the assistant, which explains it to you.

Screenshots

Code style

No explicit linting or formatting tools are configured. The code follows standard PEP 8 conventions as observed in the source files.

Build status

Tests

Example session

You:    Is this Fibonacci function correct? Run it and show me the first 10 numbers.
        def fib(n):
            a, b = 0, 1
            for _ in range(n):
                print(a, end=" ")
                a, b = b, a + b
        fib(10)

Claude: [calls run_code with your function]
        It works. The output is: 0 1 1 2 3 5 8 13 21 34

Roadmap

v0.1 — code submission, status polling, output download, compile/runtime error reporting, 24-language list
v0.2 — PyPI packaging, install and run with a single uvx hackerearth-mcp command, server.json for the official MCP registry
Published to the official MCP registry and Glama; submitted to awesome-mcp

Contributing

Issues and pull requests are welcome. If you add a tool, write its description the way you'd explain it to a smart friend who has never seen it — the assistant reads that text to decide when to use it.

License

MIT © Pratham Jain. See LICENSE.

Acknowledgments

Built on HackerEarth's code execution API and the Model Context Protocol. Thanks to HackerEarth for a genuinely developer-friendly free tier.

Tests

The repository includes a local test script test_local.py. To run the tests:

pip install fastmcp requests
python test_local.py

The GitHub Actions workflow test.yml executes the same commands on each push/pull request.

API reference

run_code(code: str, language: str) -> str Executes the supplied code in the specified language on HackerEarth and returns the program's printed output or error messages.
list_languages() -> str Returns a newline‑separated list of all supported language identifiers (e.g., PYTHON3, JAVASCRIPT_NODE).
_normalize_language(lang: str) -> str (internal) Normalizes user‑provided language names or aliases to the exact HackerEarth identifiers.
MCP registration is performed via FastMCP("hackerearth-mcp") which automatically registers the two tools above.

Created with repo-doctor

Available Tools

2 tools

list_languagesList LanguagesA

Returns the list of programming languages HackerEarth can execute, with the exact identifiers to pass to run_code. Use this when you are unsure which language name to use, or when the user asks what languages are available. Takes no inputs.

Name	Required	Description	Default
No parameters

Output Schema

Name	Required	Description
`result`	Yes

TDQS

A4.7/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are supplied, so the description carries the full burden, but for a zero-parameter read-only listing it discloses the relevant traits: it takes no inputs and returns executable language identifiers. It omits minor details such as whether authentication or rate limits apply, which keeps it just short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, front-loaded with what is returned, then when to use it, then the no-input note. Nothing is redundant and no sentence is filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need no prose explanation, yet the description still clarifies the practical value of the returned identifiers for run_code. For a trivial zero-param lookup tool this is fully adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so per the rubric the baseline is 4. The description correctly states 'Takes no inputs,' matching the empty schema with additionalProperties false.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Returns the list of programming languages HackerEarth can execute') and adds the key qualifier that the results contain 'the exact identifiers to pass to run_code', which cleanly separates it from its only sibling, run_code.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly names both trigger conditions: 'when you are unsure which language name to use' and 'when the user asks what languages are available.' This is a direct routing rule to the discovery tool rather than a guess.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

run_codeRun CodeA

Executes a snippet of code on HackerEarth's cloud servers in any of 24 languages. Use when the user wants to test code, see real output instead of predicted output, or check whether a program works. Needs the code and the language identifier, call list_languages if unsure of the exact name. Requires HACKEREARTH_KEY to be set; every run consumes the key owner's HackerEarth API quota. Returns the program's printed output and any error messages.

Name	Required	Description	Default
`code`	Yes
`language`	Yes

Output Schema

Name	Required	Description
`result`	Yes

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full behavioral burden and does so well: it discloses remote execution on HackerEarth servers, the HACKEREARTH_KEY prerequisite, and the fact that each run consumes the key owner's API quota (a real cost/rate signal). It also describes the returned artifacts (printed output and error messages), which is more than the annotations would have supplied.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, front-loaded with the core action and scope before prerequisites and return values. Slight redundancy in the trigger list ('see real output' and 'check whether a program works' overlap), but no filler sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, triggers, prerequisites, cost, and (briefly) output for a two-parameter tool that already ships an output schema. It omits execution constraints such as runtime/memory limits or sandbox restrictions, which matter for a remote code-execution tool, but the essentials for correct invocation are present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does: it names both required inputs (code, language identifier) and routes the agent to list_languages for resolving the language value. It stops short of giving format or size constraints for the code payload, so it is not exhaustive.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource (executes a code snippet) plus scope (HackerEarth cloud servers, 24 languages), which cleanly separates it from the sibling list_languages tool. An agent can tell what this tool does without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit trigger conditions ('test code, see real output instead of predicted output, or check whether a program works') and names the alternative to consult when a prerequisite is unclear ('call list_languages if unsure of the exact name'). Nothing is left to inference about when to reach for this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

2 tool updatesSep 28, 2026
- First observedlist_languages
- First observedrun_code

Deploy Server

Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

A4.6/5.0

Scored 2026-09-28 15:50 across 2 tools

Disambiguation5/5

The two tools have completely distinct purposes: one enumerates supported languages and the other executes code. There is no overlap in their intent or expected inputs, so an agent cannot confuse them.

Naming Consistency5/5

Both names follow a clean verb_noun pattern (list_languages, run_code) in consistent snake_case. The convention is predictable and readable.

Tool Count3/5

The domain (remote code execution) is narrow, so a small surface is defensible, but two tools is on the thin side. There is little room for the set to grow without adding value, making it borderline under-scoped.

Completeness4/5

The pair covers the essential lifecycle: discover a language, then run code and receive output/errors. Minor gaps exist (e.g. no explicit async job-status or quota-check operation), but core functionality is intact and workable.

Maintenance

ActivityMaintained

ResponsivenessNo issues

Resources

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hackerearth-mcp

Why

Tools

Installation

Features

Tech/framework used

Quickstart

Configuration

How it works

Screenshots

Code style

Build status

Example session

Roadmap

Contributing

License

Acknowledgments

Tests

API reference

Available Tools

Output Schema

TDQS

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Judge0 CE MCP Server

Netmind Code Interpreter

sandboxapi-mcpofficial

E2B MCP Server