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650,522 tools. Updated 2026-10-14 02:52

"Coding in Python and R" matching MCP tools:

  • Converts a causal DAG into idiomatic Python (networkx) or R (dagitty) code, plus a deep-link URL to open it in DAG Studio.
    Apache 2.0
  • Parse R or Python scripts to extract structured metadata including packages, variables, and dependencies using regex, without executing code or accessing data.
    AGPL 3.0
  • Reproduce an IPUMS microdata extract by generating ready-to-run R or Python code from the extract number, making workflows repeatable.
    MIT
  • Export routing history to CSV or JSON for analysis in Excel, Python, R, or other data tools. Get a file path and record count to track model performance.
    MIT
  • Request an NHGIS extract by specifying datasets, time series tables, shapefiles, and output format; get an extract number and status to generate reproducible R or Python code.
    MIT

Matching MCP Servers

Matching MCP Connectors

  • Run code in an isolated sandbox with persistent state across calls, supporting Python, Node.js, and R for interactive development.
    MIT
  • Scan a folder of R and Python scripts to extract metadata, register code artifacts, and aggregate variables and datasets. Returns a summary of scripts, packages, datasets, and variables.
    AGPL 3.0
  • Execute code securely in a sandboxed environment supporting Python, JavaScript, Bash, and R, for $0.005 per execution.
    MIT
  • Review R session details, Python symbols, and output files to verify installed packages and current session state at the beginning of an analysis.
    -
  • List available coding standards profiles for various tech stacks to enforce consistent code style and architecture.
    MIT
  • Verify the MCP server's Python environment, including executable path, version, and installed packages, to ensure correct setup and troubleshoot missing dependencies.
    BSD 3-Clause
  • Run Python, SQL, Scala, or R code on Databricks clusters or serverless compute, then get status or cancel pending executions and return results.
    Apache 2.0
  • Execute Python code with flexible execution modes including inline for speed and subprocess for isolation. Use to run Python scripts, test code snippets, or maintain session state across executions.
    MIT
  • Run Python code with persistent variables across calls. Assign data to named variables for later reuse; use 'result' to return output to the caller.
    MIT
  • Retrieve Python package metadata from PyPI, including version, license, and Python compatibility. Use to evaluate library maintenance and compatibility before integration.
    MIT