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Code Interpreter

CodeInterpreter extends Python-based coding environments with integrated data analysis, enabling developers to run scripts, visualize results, and prototype solutions inside supported platforms

Verdict

Code Interpreter gives your team a sandboxed Python environment inside Switchy. @mention it to run code, manipulate files, or execute terminal commands without leaving the conversation. Data analysts use it to prototype transformations on uploaded CSVs. Engineers test snippets before committing. Product managers ask it to chart survey results or parse JSON exports. The sandbox persists across messages in a Space, so you can iterate on scripts, read output, and refine logic in real time. No local setup required, but remember: it's Python only, and the environment resets when you close the Space.

Common use cases

Prototype data transformations on uploaded CSVs
Test Python snippets before committing code
Generate charts from survey or analytics exports
Parse and validate JSON or XML files
Run shell scripts to batch-process files

Integration

Vendor: Code Interpreter
Category: developer-tools
Auth: NONE
Tools: 5
Composio slug: codeinterpreter

Tools

Create Sandbox
Create a sandbox to execute python code in a jupyter notebook cell. this is useful for agents to communicate, execute code, see output, read files, write files, etc. it's like you own personal computer, but in the cloud. use /home/user fold
Execute Code
Execute python code in a sandbox and return any result, stdout, stderr, and error. use /home/user folder to write/read files. try to not use plt.show() as the code is executed remotely. use files for image/chart output instead.
Get File
Get a file from the sandbox and returns the file. the files should be read from /home/user folder.
Run Terminal Command
Run a command in the terminal and returns the stdout, stderr, and error code. use /home/user folder to write/read files.
Upload File
Upload a file to the sandbox environment. the files should be uploaded to the /home/user folder.

Setup

Setup guide

11. Open any Space in Switchy and type '@Code Interpreter' in the message field — the integration appears automatically with no authentication required. 2. Send a message asking it to create a sandbox (e.g. '@Code Interpreter create a sandbox and run print("hello")'). 3. The MCP spins up a fresh Jupyter-style environment and returns output, stdout, and any errors. 4. Upload files by asking '@Code Interpreter upload this CSV' and attaching the file, or reference files already in /home/user from previous messages. 5. Test the connection by running a simple command like '@Code Interpreter run ls in the terminal' — you should see the /home/user directory structure. 6. Continue the conversation: each code block or terminal command runs in the same sandbox until you leave the Space, so variables and files persist between messages.

What teammates see: by default, memories from Code Interpreter are scoped to the Space (PROJECT visibility) - you can mark any memory PRIVATE or share it ORG-wide.

Works well with

Top models

Compatibility data appears once enough Spaces have used this MCP together with a given model.

How Switchy teams use it

Not enough Spaces yet to publish anonymised usage stats (we require ≥ 50 Spaces per week).

Starter prompts

Clean CSV Data

@Code Interpreter I'm uploading a CSV with customer records. Remove duplicate emails, fill missing phone numbers with 'N/A', and save the cleaned file as customers_clean.csv in /home/user.

Open in a Space →

Chart Survey Results

@Code Interpreter I have a CSV with a 'rating' column (values 1-5). Create a bar chart showing how many responses fall into each rating, and save it as ratings_chart.png.

Open in a Space →

Validate JSON Schema

@Code Interpreter I'm uploading a JSON file. Validate that it has 'id', 'name', and 'created_at' fields in every object, and list any entries that don't match.

Open in a Space →

Batch Rename Files

@Code Interpreter Run a shell command to rename all .txt files in /home/user by adding today's date as a prefix (format YYYYMMDD_filename.txt).

Open in a Space →

Parse Log File

@Code Interpreter I'm uploading a server log. Find all lines containing 'ERROR', count occurrences of each error code, and show the top 5 in a table.

Open in a Space →

Example outputs

Illustrative - representative of the model's voice and quality, not literal recordings.

Prompt

@code-interpreter can you analyze this CSV of monthly sales data and create a bar chart showing the top 5 products by revenue?

Output

I've created a sandbox and analyzed your sales data. The top 5 products by revenue are: Enterprise License ($847K), Pro Subscription ($623K), Starter Plan ($412K), Add-on Pack ($287K), and Consulting Hours ($156K). I've generated a bar chart saved to /home/user/top_products.png showing these results with revenue on the y-axis. The chart uses a clean color scheme and includes data labels for easy reading. Would you like me to adjust the visualization or perform additional analysis on this dataset?

Notes

This example demonstrates the MCP's ability to execute Python code for data analysis and visualization. The sandbox environment persists across multiple interactions within a session, so you can iteratively refine analysis. Files created in /home/user remain accessible for download or further processing. Note that the sandbox has standard Python data science libraries (pandas, matplotlib, numpy) pre-installed.

Prompt

@code-interpreter I need to batch rename 50 image files following the pattern 'product_001.jpg' through 'product_050.jpg'. Can you write and run a script for that?

Output

I've executed a Python script using os.rename() to batch rename your files. The script successfully processed all 50 images in /home/user, renaming them from their original names to the sequential pattern you specified (product_001.jpg, product_002.jpg, etc.). The operation completed without errors. I've also generated a log file at /home/user/rename_log.txt that maps each original filename to its new name, in case you need to reference or reverse the changes. All renamed files are ready in the same directory.

Notes

This showcases the MCP's terminal command and file system capabilities. The sandbox provides a safe environment to test file operations before running them on your local machine. Be aware that file operations are permanent within the sandbox session—there's no automatic undo. The sandbox resets between sessions, so download any files you need to preserve before ending your Space conversation.

Prompt

@code-interpreter review the Python function I just uploaded and suggest optimizations for performance. It processes a list of 100k user records.

Output

I've analyzed your uploaded function and identified three optimization opportunities. First, you're using a nested loop (O(n²) complexity) for matching records—switching to a dictionary lookup would reduce this to O(n). Second, the string concatenation in the loop creates unnecessary intermediate objects; using a list with join() would be more efficient. Third, you're reading the entire dataset into memory at once; for 100k records, consider using pandas chunking or a generator pattern. I've created an optimized version at /home/user/optimized_function.py that implements these changes. In my benchmark test with mock data, the optimized version runs 47x faster (0.8s vs 38s).

Notes

This example highlights the MCP's ability to combine file upload, code execution, and AI reasoning for code review tasks. The sandbox can run benchmarks and tests to validate suggestions empirically. Keep in mind that the sandbox has memory and CPU limits—extremely large datasets or long-running computations may timeout. The MCP can't access external APIs or databases from the sandbox, so testing is limited to self-contained code.

Use-case deep-dives

Data analysis sprint review prep

When sandboxed Python beats asking engineers for CSV exports

A 5-person product team runs weekly sprint reviews where the PM needs to slice usage data by cohort or feature flag. Without this MCP, the PM pings an engineer, waits 20 minutes for a Jupyter notebook export, then asks for a tweak. With Code Interpreter, the PM describes the question in Switchy, the agent spins up a sandbox, reads the uploaded CSV, runs pandas groupby logic, and returns a chart—all in under a minute. The trade-off: if your data lives in Snowflake or needs OAuth to a warehouse, this MCP can't reach it; you'd need a database-specific integration instead. But for ad-hoc analysis on files under 50MB that anyone on the team can upload, this MCP turns every sprint review into a live data conversation.

Customer support script debugging

Why support teams use this to test regex and JSON transforms

A 3-person support team at a SaaS company gets tickets where customers paste malformed JSON or ask if a regex will match their use-case. The support lead used to forward these to engineering or guess in the reply. Now they paste the customer's sample into Switchy, ask the agent to validate it in a Code Interpreter sandbox, and get a working snippet or error message in 10 seconds. The MCP's terminal command tool even lets the agent test shell one-liners for bulk file renames or log parsing. The boundary: if the script needs to call your production API or access secrets, the sandbox can't do that—this is for side-effect-free validation only. For teams answering 5+ technical tickets a day, this MCP pays for itself by keeping engineers focused on roadmap work.

Marketing campaign UTM report generation

When no-code teams need Python without installing Python

A 2-person growth team runs Facebook and Google ads, exports campaign CSVs weekly, and needs to merge them by UTM parameter to see true cost-per-lead. Neither person codes, and asking engineering to build a dashboard takes a quarter. With this MCP in Switchy, they upload both CSVs, describe the join logic in plain English, and the agent writes and runs the pandas merge in a sandbox—returning a clean CSV they paste into Google Sheets. The upload and get-file tools make this a round-trip workflow with no local Python install. The catch: if your exports are over 100MB or need live API pulls, the sandbox will time out or lack credentials. But for weekly batch reporting under 10MB per file, this MCP turns a 2-hour manual task into a 2-minute conversation.

Frequently asked

What does the Code Interpreter MCP do in Switchy?

It spins up isolated Python sandboxes where your AI agents can run code, read and write files, and execute terminal commands. Think of it as giving your team's AI a scratch environment for data analysis, file processing, or testing scripts without touching your local machine. Each sandbox runs in /home/user and persists until you tear it down.

Do I need to authenticate or install anything to use it?

No authentication required. The MCP handles sandbox provisioning automatically when an agent requests it. You don't need API keys, OAuth flows, or admin permissions. Just connect the MCP in Switchy and your agents can start creating sandboxes on demand.

Can it run languages other than Python or install custom packages?

Python only. The sandboxes run Jupyter-style cells, so you can pip install packages at runtime, but you're limited to what works in that Python environment. If you need Node, Go, or compiled binaries, you'll hit a wall. Terminal commands work for basic shell tasks, but this isn't a full VM.

Why use this instead of running Python locally or in a notebook?

Your agents can't touch your local filesystem or shell. This MCP gives them a safe, disposable environment that doesn't require you to share credentials or expose your machine. It's faster than spinning up a cloud VM for one-off tasks, and the agent can manage the full lifecycle—create, execute, retrieve files, destroy—without human handoff.

Does each team member get their own sandbox, or do we share one?

Sandboxes are created per request, not per user. If two agents (or the same agent in different threads) call Create Sandbox, they get separate environments. There's no built-in limit in the MCP itself, but check your Switchy plan for compute quotas. Sandboxes don't auto-expire, so clean up after long-running tasks.