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+---
+title: Announcing mcp-repl
+date: 2026-06-30
+people:
+  - Tomasz Kalinowski
+description: >
+  A sandboxed R and Python REPL for MCP-capable agents
+image: mcp-repl-hex.png
+image-alt: >
+  Hex logo for mcp-repl showing a robot typing at a terminal inside a
+  hexagon outline.
+topics:
+  - Artificial Intelligence
+  - Best Practices
+software:
+  - mcp-repl
+languages:
+  - R
+  - Python
+source: ai
+tags:
+  - MCP
+  - Agents
+hidesubscription: false
+---
+
+Most AI agents can run code. That is not the same as having a useful R
+or Python session.
+
+R and Python work well because they are interactive. A session
+accumulates objects, loaded packages, warnings, plots, documentation
+lookups, debugging frames, and partial results. When an agent is doing
+real data work, that continuity matters.
+
+`mcp-repl` is a new open source MCP server from Posit that gives
+MCP-capable agents a private, sandboxed, persistent R or Python REPL.
+
+It is built for model-facing workflows rather than human-facing
+consoles. The session keeps state across tool calls, returns plots
+through MCP, renders help in-band, supports debugger modes and
+stdin-driven nested REPLs, keeps large outputs bounded, and provides
+explicit interrupt and reset controls.
+
+The goal is narrow: give agents the interactive affordances that make R
+and Python useful for real data work, without turning the runtime into
+an unrestricted shell.
+
+## Why agents need more than shell commands
+
+Many agents interact with R and Python through batch commands:
+
+```sh
+Rscript -e '...'
+python -c '...'
+```
+
+That is fine for isolated probes. It is a poor fit for exploratory
+analysis, project debugging, and long-running work.
+
+Each command starts over. The agent has to reload data, recreate
+objects, re-import packages, and reconstruct context instead of
+continuing from the previous step.
+
+A terminal session can preserve state, but it usually leaves the agent
+with an unstructured stream of text. The agent may need to poll for
+output, infer whether the interpreter is ready, guess how to handle
+continuation prompts, and work around pagers, plots, help systems, and
+debuggers.
+
+`mcp-repl` provides a structured REPL interface for this kind of work.
+It keeps the R or Python process alive across tool calls, captures the
+parts of the session that matter to the model, and reports when the
+interpreter is ready for the next input.
+
+## A typical agent workflow
+
+An agent using `mcp-repl` can move through an analysis in small steps
+without restarting the runtime each time.
+
+For example, you might ask an agent to analyze last week's sales data.
+The agent can load the data once, inspect the shape and missingness,
+compare it to recent history, generate plots, fit a quick model, read
+documentation for an unfamiliar function, and refine its findings before
+returning a concise report.
+
+That makes the interaction look less like repeated command execution and
+more like a careful analyst working through a live R or Python session.
+
+## A real REPL, not a prompt parser
+
+`mcp-repl` runs R or Python as a long-lived worker accessible through an
+MCP interface.
+
+The agent sends code through a `repl()` tool. The worker evaluates it,
+captures output, and reports when the interpreter is ready for the next
+step.
+
+Because `mcp-repl` owns enough of the REPL loop, it does not need
+prompt-string polling, fixed sleeps, or output-timing heuristics. The
+server knows when the interpreter is idle and when a result has settled.
+
+Unlike a Jupyter-style kernel, `mcp-repl` drives the interpreter through
+its native interactive interface. That means it can handle not only
+complete expressions, but also the line-by-line inputs used in ordinary
+R and Python sessions.
+
+That matters for interactive features that batch code runners often
+handle poorly:
+
+- R `browser()` sessions
+- Python `pdb` sessions
+- nested interactive modes, such as `IPython` and
+  `reticulate::repl_python()`
+- continuation prompts
+- help pages and pagers
+- warnings and errors
+- plots
+
+
+These are normal parts of R and Python work. `mcp-repl` exposes them to
+agents through a compact MCP interface instead of forcing the model to
+reverse-engineer a terminal transcript.
+
+## Designed for model-facing output
+
+Human terminals and model contexts have different constraints.
+
+A human can scroll through thousands of lines and skim visually. A model
+usually needs compact, ordered, bounded output with a clear indication
+of what happened and what is available next.
+
+`mcp-repl` is designed around that constraint. It uses smart echo
+behavior to avoid cluttering the transcript when the input is already
+obvious, captures plots through MCP, and keeps large outputs bounded.
+
+Instead of flooding the model context, oversized results are written to
+a structured bundle containing the transcript and any plot files. The
+agent can inspect that bundle on demand.
+
+This keeps ordinary interactions concise while preserving access to the
+full output when it matters.
+
+## Sandboxed by default
+
+Agents can run code quickly and repeatedly. That makes execution policy
+part of the product, not an afterthought.
+
+`mcp-repl` runs the backend in a sandbox by default. Network access is
+disabled unless configured. Writes are constrained to the workspace and
+session temporary paths. On supported platforms, the sandbox is enforced
+with OS-level primitives at the process level rather than with prompt
+instructions.
+
+The default policy is useful for project work: the agent can read and
+write within the working area, create session temporary files, generate
+plots, and run analysis code, but it does not receive an unrestricted
+shell by default.
+
+For clients that can provide sandbox metadata, such as Codex, `mcp-repl`
+can inherit the client's per-call sandbox policy. For other MCP clients,
+it can be configured with an explicit policy such as workspace-write.
+
+## A small MCP surface
+
+The MCP surface is deliberately small.
+
+The core tool is `repl`:
+
+```json
+{
+  "input": "1 + 1\n",
+  "timeout_ms": 10000
+}
+```
+
+Interrupts and resets are explicit session controls. A Ctrl-C prefix
+requests an interrupt and leaves the session running. A Ctrl-D prefix
+requests a reset, shuts down the current worker through a bounded
+shutdown path, and starts a fresh session.
+
+Keeping the API small is intentional. Most of the complexity belongs
+below the interface, where `mcp-repl` supervises worker lifecycle,
+sandbox policy, output ordering, image capture, timeouts, interrupts,
+resets, and oversized-output bundles.
+
+The agent gets a simple tool. The runtime handles the messy parts.
+
+## What the agent gets
+
+`mcp-repl` exposes the parts of R and Python that matter during
+interactive work:
+
+- stateful execution across tool calls
+- bounded, model-oriented output
+- smart echo behavior for concise transcripts
+- plot capture through MCP
+- R help, vignettes, and manuals in-band
+- Python help through `help()`, `dir()`, and `pydoc`
+- support for R `browser()`, Python `pdb`, and nested REPLs like IPython
+- structured bundles for oversized output
+- explicit interrupt and reset controls
+- sandboxed execution by default
+
+These features are not a new programming model. They are the existing R
+and Python workflow adapted to an agent interface.
+
+## Where it fits
+
+`mcp-repl` is useful when an MCP-capable agent needs to do R or Python
+work with less supervision, especially in unattended or lightly
+supervised workflows.
+
+Use `mcp-repl` when you want an agent to:
+
+- ask an agent to produce recurring reports, such as analyzing last
+  week's sales data, finding what changed, and drafting a report that
+  highlights fresh, surprising, or concerning trends
+- give an evaluation harness a realistic R or Python runtime for
+  measuring agent capability on data-analysis tasks, using tools such as
+  [Inspect](https://inspect.aisi.org.uk)
+- ask an agent to explore a dataset, check data quality, identify strong
+  signals, and suggest the next analyses worth running
+- ask an agent to debug an R or Python project by reproducing a failing
+  example, inspecting live objects, stepping through the debugger, and
+  proposing a minimal fix
+- ask an agent to prepare artifacts for review by privately iterating on
+  analysis code, plots, and summary tables before returning final
+  results with caveats
+
+Because the runtime may be used unattended, the sandbox is part of the
+core design rather than an optional wrapper around it.
+
+`mcp-repl` is also useful in general-purpose agent harnesses.
+MCP-capable tools such as Claude Code and Codex are not primarily built
+around data analysis, but they are often used on R and Python projects.
+Adding `mcp-repl` gives those agents a live, persistent runtime instead
+of only isolated shell commands.
+
+## How it relates to Posit Assistant
+
+`mcp-repl` and Posit Assistant address different parts of AI-assisted
+data work.
+
+`mcp-repl` is a plug-in runtime for autonomous or lightly supervised
+agents. It works through MCP and gives existing agents a private,
+sandboxed R or Python REPL.
+
+Posit Assistant is an integrated, human-in-the-loop product. It combines
+a development environment with agent-facing execution support, so the
+user and model can work with shared project context.
+
+Both are about making R and Python better environments for AI-assisted
+data work. `mcp-repl` focuses on autonomous work in a private runtime.
+Posit Assistant focuses on close collaboration between a human and a
+model.
+
+## Getting started
+
+Install from PyPI. The package is named `posit-mcp-repl` and exposes the
+`mcp-repl` executable:
+
+You can install with `uv`:
+
+```sh
+uv tool install posit-mcp-repl
+```
+
+
+Prebuilt binaries are also available for macOS, Linux, and Windows. On
+macOS or Linux, you can install with:
+
+```sh
+curl -fsSL https://raw.githubusercontent.com/posit-dev/mcp-repl/main/scripts/install.sh | sh
+```
+
+On Windows PowerShell:
+
+```powershell
+irm https://raw.githubusercontent.com/posit-dev/mcp-repl/main/scripts/install.ps1 | iex
+Install-McpRepl
+```
+
+You can also install from source with Cargo:
+
+```sh
+cargo install --git https://github.com/posit-dev/mcp-repl --locked
+```
+
+Once `mcp-repl` is installed, you can configure `codex` or `claude` to
+use it as an mcp client:
+
+```sh
+mcp-repl install
+```
+
+By default, this writes entries for both R and Python for supported
+clients. You can also target a specific client or interpreter:
+
+```sh
+mcp-repl install --client codex
+mcp-repl install --client claude
+mcp-repl install --client codex --interpreter r
+mcp-repl install --client claude --interpreter python
+```
+
+Once configured, the MCP client exposes the `repl` tool for running code
+in the session.
+
+## Open source
+
+`mcp-repl` is open source. Project repository:
+<https://github.com/posit-dev/mcp-repl>
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+---
+color: '#4B5563'
+description: Persistent R and Python REPL sessions for coding agents
+github: posit-dev/mcp-repl
+image: logo.png
+languages:
+- Rust
+- R
+- Python
+latest_release: '2026-05-18T23:09:38+00:00'
+people:
+- Tomasz Kalinowski
+title: mcp-repl
+topics:
+- Artificial Intelligence
+- Best Practices
+website: https://github.com/posit-dev/mcp-repl
+
+include:
+  languages:
+  - R
+  - Python
+
+override:
+  description: Persistent R and Python REPL sessions for coding agents
+  website: https://github.com/posit-dev/mcp-repl
+
+external:  # updated automatically, do not edit
+  description: ''
+  first_commit: '2026-02-12T15:26:11+00:00'
+  forks: 4
+  languages:
+  - Rust
+  last_updated: '2026-05-20T08:05:17.523929+00:00'
+  latest_release: '2026-05-18T23:09:38+00:00'
+  license: Apache-2.0
+  people:
+  - Tomasz Kalinowski
+  repo: posit-dev/mcp-repl
+  stars: 32
+  title: mcp-repl
+  website: ''
+---
+
+mcp-repl is an MCP server that gives coding agents persistent R and Python sessions across tool calls. It lets an agent load data once, inspect objects, read help, make plots, and keep iterating in a live REPL instead of rebuilding state for every command.
+
+The server is designed for agent workflows that need interactive language features: persistent variables, in-band help, plot capture, interrupts, resets, curated large-output handling, and sandboxed execution.
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