IRP

Name: IRP
Author: Noshadi-sec

by Noshadi-secversion 0.6.0i★ –iRuns on your computer

Registry nameio.github.Noshadi-sec/irp

Install

Overview

Description from the maker (original English)

Turn a research paper into working code and prove, claim by claim, how close it gets.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "irp": {
      "command": "uvx",
      "args": [
        "irp-agents",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add irp -- uvx irp-agents mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "irp": {
      "command": "uvx",
      "args": [
        "irp-agents",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "irp": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "irp-agents",
        "mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account Noshadi-sec, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 5668 servers in this category
Published to the registry more recently than 71% of the other 5668 servers in this category
Basic checks equal to or better than 100% of the other 5668 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is IRP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.6.0) reached the registry 17 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Computer or cloud★ 64,224MITUpdated yesterday

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Codebase Memory

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Runs on your computer★ 48,148MITUpdated 21 days ago

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Exa

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Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 2 months ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

English · 简体中文 · 日本語 · 한국어 · Español · Italiano

IRP: Build any paper. Prove every claim.

One MCP server and CLI that lets your coding agent turn a research paper into working code, run it, and prove claim by claim how close it gets.

Give it a paper. Get back working code and a report you can check without trusting the agent.

Quick start · How IRP works · The report · What you can reproduce · FAQ · Documentation

uvx irp-agents setup

[!IMPORTANT]

Get IRP from Noshadi-sec/irp or the

irp-agents package on PyPI. Follow the

installation and release links from these official sources. A similar

repository name or copied README does not establish that a download is official.

Found a paper with no code, or code you cannot trust? Ask your agent to build it with IRP. The agent reads the paper, records every number it claims, writes the implementation, runs it and compares the results. IRP does the fetching, running and record keeping, and refuses the shortcuts: a number typed by hand never counts, a shortened run never counts, and the report is written only from the evidence log.

IRP connects your agent (Claude Code, Codex or Cursor) to tools for paper intake, environments, runs, measurements and verdicts. You can use the same tools from your terminal. Everything runs locally; IRP never calls a model, and data goes only where you send it.

Quick start

Set up your agent

With uv installed, run:

uvx irp-agents setup

Choose your agents, review the proposed changes, and approve them. Setup adds IRP's MCP server and its workflow skill, with backups of existing configuration. Restart your agent afterward.

Setup supports Claude Code, Codex and Cursor. Other MCP clients can use manual registration.

Ask your agent

Build arXiv 2501.12345 and check Table 2.

You get back a working code folder plus a report: every claim, the paper's number, your number, and a verdict.

Use the terminal

Every tool is also a command. Fetch a paper into a new study and read its outline:

irp fetch 2501.12345 --study ~/studies/example

irp section index --study ~/studies/example

To install the irp command for regular use:

uv tool install irp-agents

irp --help

See the CLI guide for the full workflow from the terminal.

Check readiness

irp doctor

Doctor checks Python, uv, git, Docker, GPUs, disk space and your agents' registrations, and changes nothing.

Update IRP

IRP changes quickly. For an installed command:

irp update

With uvx, refresh your agent setup with uvx irp-agents@latest setup, review the plan, and restart your agent.

How IRP works

Your agent thinks; IRP acts and keeps the receipts. The agent reads the paper, writes code and decides what to run. Every action goes through one core, so the CLI and the MCP server behave the same, and every step lands in an append-only, hash-chained evidence log. The report is built from that log alone.

Stage	Tool	What it does
Intake	`fetch_paper`	Downloads the arXiv LaTeX source or the PDF, hashes it, splits it into sections
Intake	`get_section`, `get_page_image`	Returns one section's text with file and line numbers, or one page as an image for equations and figures
Claims	`add_claim`, `list_claims`	Records each claim: metric, value, where it appears, and the success bar
Plan	`add_guess`	Records a missing detail, the choice made, and why
Plan	`map_method`	Links an equation or algorithm step to a file and line
Build	`create_env`	Makes a uv environment or Docker image and writes the lock file
Run	`run`, `run_status`	Runs one command; saves output, exit code, time, memory, seed, commit and files
Run	`run_remote`	Same on a paid GPU provider, after a cost estimate and approval
Measure	`extract_metrics`	Pulls numbers from logs and result files, with file and line
Judge	`compare`, `add_note`	Puts claim and measurement side by side and gives one of four verdicts
Report	`report`, `verify`, `study_status`	Writes report.md and report.json from the log; rechecks the hash chain

The workflow skill gives the agent six hard rules, and the tools enforce them wherever they can:

Never type, estimate or copy a number as a result.
Never change the paper's method to make a run succeed; record a guess instead.
Report "could not run" with the blocking error instead of pushing through.
Run the smallest faithful version first; shortened runs never count.
Record every claim before writing any code.
Ask before any paid compute.

The report

The report is the product: a person must be able to check every verdict without trusting the agent. It has five parts: the claim table, four verdicts (reproduced, close, not reproduced, could not run), the guess list, the method map from equations to code lines, and provenance (arXiv ID and version, file hashes, git commit, lock files, GPU, seeds, every command with its exit code, and a verify command that rechecks the hash chain).

The agent cannot finish until every claim has a verdict with evidence.

Example report.md (made-up numbers):

# Reproduction report: Example Paper (arXiv 2501.12345v2)

Result: 2 reproduced, 1 close, 1 could not run (of 4 claims)

Claim	Where	Paper	Ours	Verdict
c1 Test accuracy	Table 2, row 3	94.1%	93.8% (3 seeds, std 0.2)	close
c2 Training loss	Figure 4	0.31	0.31 (3 seeds)	reproduced
c3 Speed-up	Section 5.2	2.0x	2.1x	reproduced
c4 ImageNet top-1	Table 3	81.2%	not run: needs 8 GPUs for 2 days	could not run

Guesses

g1 Warmup length not stated: used 5 epochs. Why: as in cited paper [12].

Method map

Eq. 3 (loss) -> code/src/loss.py:41
Algorithm 1, line 4 -> code/src/train.py:88

Provenance

Paper: arXiv 2501.12345v2, source and PDF hashes
Code: git commit 3f2a9c1 (clean)
Commands logged: 37, 37 with an exit code
Evidence log: 112 records, head sha256:…, verified
Verify: irp verify --study ~/studies/example

See evidence and report for every field.

What you can reproduce

IRP requires Python 3.11+ on macOS or Linux. Additional tools depend on the paper:

Need	What IRP uses	Requirements and guide
Paper intake	arXiv LaTeX source first, the PDF as fallback, page images for math and figures	Network access to arXiv; paper intake
Better PDF structure	Docling for text and tables	Optional `irp-agents[docling]`; paper intake
Environments	uv virtual environments with lock files, or Docker images for isolation	uv, or Docker; environments
Compute	Your CPU or GPU; Modal GPU sandboxes after a cost estimate and approval (experimental)	Optional `irp-agents[modal]`; compute
Agents	Claude Code, Codex, Cursor; any MCP client by hand	installation

IRP supports computational papers: machine learning, algorithms and simulations. It does not support wet-lab work or anything weapons-related.

Benchmark

Five light papers — official code exists, each runs on a CPU in minutes — rebuilt from the paper alone and checked claim by claim. Every number comes from a verified evidence log; re-check any study with irp verify --study benchmarks/studies/ .

Paper	Claims	Result
Fashion-MNIST (1708.07747)	3	1 reproduced, 1 close, 1 not reproduced
KMNIST (1812.01718)	2	both reproduced
MNIST-1D (2011.14439)	1	reproduced
fastText (1607.01759)	1	reproduced (92.86% vs 92.5%)
QMNIST (1905.10498)	1	reproduced (2.95% vs 2.95%)

Across 8 claims: 6 reproduced, 1 close, 1 not reproduced (87.5% reproduced or close), ~160s and $0 of paid compute per paper. Selection criteria and the full set are in benchmarks/golden/papers.json; the protocol and summarize.py are in benchmarks/.

If you build a paper with IRP, share the report in an issue with the paper, your question, and what you found.

FAQ

Which agents can use IRP?

Any agent that supports local MCP servers. Setup configures Claude Code, Codex and Cursor; other clients can use manual registration.

Does IRP call an AI model or upload my paper?

No. Your agent is the brain; IRP never calls a model and has no API key. It downloads papers from arXiv, installs packages through uv or Docker, and runs code on your machine. Your agent receives the tool results, and its model provider has its own data policy.

Is running a paper's code safe?

Running code is running code. A uv virtual environment is not a sandbox: the code runs with your permissions. Use create_env with backend: "docker" for code you did not write or do not trust, and runs mount only the study's code directory. See security.

What does "not reproduced" mean?

That this run, with the recorded guesses, did not reach the paper's number within the stated bar. The report says "not reproduced by this run" and lists every guess. It is evidence about one attempt, not a verdict on the paper; contact the authors before publishing it.

The paper has official code. Should I use IRP?

IRP builds missing code from the paper. To check that existing code reproduces its README's numbers, a tool such as paper-repro fits better. IRP is still useful for testing a paper by hiding its code and comparing your rebuild with it afterwards.

Can IRP spend money?

Only on paid GPU providers, only after you record a price and a budget at a terminal with irp budget, and only after you approve the estimate the agent shows you. The run's time limit equals the estimated hour

Only the beginning is shown; read the rest in the repo

IRP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

IRP: Build any paper. Prove every claim.

One MCP server and CLI that lets your coding agent turn a research paper into working code, run it, and prove claim by claim how close it gets.

Quick start

Set up your agent

Ask your agent

Use the terminal

Check readiness

Update IRP

How IRP works

The report

Guesses

Method map

Provenance

What you can reproduce

Benchmark

FAQ