"Coding in Python and R" matching MCP servers:

Enables coding agents to drive and inspect running Shiny apps (R and Python) by setting inputs, clicking, reading outputs, and getting detailed error information.

Enables Claude to interact with R package development workflows, including CRAN metadata lookup, dependency checking, testing, documentation validation, and release automation.

MCP coding agent with read-only tools and one paid tool for Paybond sandbox integration, enabling quick smoke testing and agent operations.

Provides safe repository operations, skill reading, command execution, and test detection for coding agents over stdio.

MCP server for the Ghost Inspector API that lets agents inspect and manage end-to-end browser tests, offering read-only tools for inventory, module usage, and stale test analysis, plus opt-in guarded write operations.

Read visual bug reports from Feedbug in your AI coding agent: screenshot, console logs, failed network requests, session replay and the DOM context of the element the tester clicked. Trace the bug to its source file, comment and resolve without leaving the editor.

Lets AI agents visually inspect web elements, test CSS edits in real-time, and iterate until pixel-perfect, functioning like browser DevTools for debugging UI issues.

Static scanner for MCP/agent-gateway readiness anti-patterns, providing a 7-dimension R/Y/G score by scanning code and config for common failure modes.

Enables AI coding tools to run CheckMyLaunch scans on GitHub repos or live URLs and retrieve findings and repair prompts directly within the chat.

Provides an in-memory notebook interface with tools to create, retrieve, list, update, and delete notes, designed to test agent behavior through realistic MCP tool interactions.

Enables AI agents to hire verified human operators for tasks requiring physical presence, human perception, or judgment, such as real-world verification, product testing, and data collection.

Audits a module against its test suite and reports the cases the tests are structurally unable to see, then proves each gap with a real failing test rather than a warning. Covers Python, TypeScript, JavaScript, Java, Rust and Go.

Enables coding agents to read Squally CI data, including test results, flakiness, and failure diagnostics.

Renders HTML in a headless browser and detects layout defects numerically, such as text overlaps, placeholder leftovers, and element overflow, without requiring baseline images.

Connects AI coding assistants to Gaffer test history and coverage data to analyze project health, debug failures, and identify untested code areas. It enables tools to track test stability, cluster failures by root cause, and monitor code coverage trends across projects.

Visual CVE audit dashboard for npm, Python, Go, and Rust projects. Scans your project manifests (package-lock.json, requirements.txt, go.sum, Cargo.lock) against OSV.dev live data, opens a browser dashboard for human review, then applies fixes only after explicit confirmation. Supports multi-service monorepos in one command.

Enables chat assistants to insert graded coding exercises (Python, JavaScript, TypeScript, SQL) with hidden tests, and returns failed attempts to the conversation so the tutor can explain the learner's specific mistake.

Vendor-neutral CLI and MCP server that verifies the token and cost savings AI-coding-agent context-reduction proxies actually deliver, measured against a real labeled corpus.

Enables agents to measure a live page or Figma frame against a reference design, reporting exact numerical deviations in layout, spacing, typography, colors, states, and animations before any baseline exists.

Enables coding agents to gate code modifications behind an evidence check, preventing unnecessary edits by requiring reproduction of the problem before allowing changes.