"Editing IDE Code Files in JetBrains" matching MCP servers:

MCP server that provides AI coding assistants with access to call graphs, data flows, and security analysis for multi-file vulnerability detection.

Enables coding agents to develop on one machine and verify results on another, using branch-bound runbooks, isolated checkpoints, and structured receipts to guide repair iterations.

An MCP server to create secure code sandbox environment for executing code within Docker containers.

A local MCP server that indexes codebases into Neo4j and provides tools for code analysis, dependency visualization, and impact assessment.

An MCP server for fast local source code searching using Zoekt, enabling regex and substring queries across indexed directories.

Turns a codebase into a queryable graph with semantic search, call graphs, and control/data flow analysis, served to AI coding agents via the Model Context Protocol.

AI modifies a function but misses 5 callers that also need updating. crag fixes this — it builds precise call graphs via static analysis, so AI knows exactly what's affected before making changes.

RegressGuard is an MCP server that records a known-good baseline and lets AI coding agents detect regressions (broken API contracts, failing tests, schema changes) during their edit loop, allowing them to self-correct before committing.

An MCP server that provides persistent memory for AI agents, enabling context retention across sessions and supporting vibe-loop workflows where agent promises and deliverables are tracked and validated.

An MCP server that gives AI agents structured read/write access to a story-based project backlog. Agents can list stories, read content, update status, and append notes — all backed by plain markdown files that live inside your project repository. There is no shared server. The backlog files live in your repo under requirements/, committed and versioned alongside your code

All-in-one homelab management MCP server. Docker monitoring, volume backup/restore (with compose and env files), Wake-on-LAN, network scanning, and multi-server SSH management.

A TypeScript server implementing the Model Context Protocol that provides secure code execution in isolated Docker containers, allowing LLM applications to safely run Python, Go, or JavaScript code snippets.

Execute code in 8 programming languages via gVisor-sandboxed Docker containers. Supports Python, JS, TS, Go, Java, C++, C, and Bash.

Provides git-aware safe file operations, allowing AI agents to delete files while protecting git-tracked source code from accidental deletion.

An MCP server for semantic code search & navigation that helps AI agents work efficiently without burning through costly tokens. Instead of reading entire files, agents can search conceptually and jump directly to the specific functions, classes, and code chunks they need.

Connect your Umami Analytics to any MCP client to derive insights from natural language.

Enables sending emails via Amazon SES, SMS/MMS/RCS messages through AWS End User Messaging, and sharing files via CloudFront-signed download links, all from AI assistants like Claude Code or Claude Desktop.

Enables finding files and knowing their location and permissions, organizing folders, moving and copying content, transferring files in and out, managing sharing with transparency, tracking changes, and handling shared drives.

Enables reading and writing MS Excel files, including cell values, formulas, sheet creation, copying, formatting, and on Windows, live editing and screen captures.

An MCP server that stores notes attached to specific code locations and keeps them attached as code moves, enabling coding agents to read context before editing and leave handoff notes for future sessions.