"Debugging Code and Granting Access to Docker" matching MCP servers:

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

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.

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.

CNN's Fear & Greed index for the US stock market: current score and rating, the seven component indicators, and about a year of daily history. Go standard library only, no API key required.

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.

MCP server that enables AI assistants to drive systematic literature review workflows, including screening, downloading, converting, and reviewing papers through natural language conversation.

KSM MCP is a Model Context Protocol (MCP) server that acts as a secure intermediary between AI language models (like Claude) and Keeper Secrets Manager (KSM). It allows AI agents to manage your KSM secrets-such as listing, creating, retrieving, and deleting records and folders-while protecting your

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.

Enables reading and searching API documentation from YApi instances, allowing AI models to access interface definitions, project API lists, and search through API endpoints using YApi URLs or project IDs.

MCP server that exposes Kafka debugging to an LLM: find messages, measure lag, unblock consumers. Read-only endpoints, previews before writes.

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.

Enables AI assistants to access SAP ADT APIs for reading, writing, debugging, deploying, and testing ABAP code through natural language or DSL automation.

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

Code-pinned team memory for AI coding agents — typed artifacts (Decision/Analysis/Debug/Task), MCP-native workflow, self-host with Docker Compose.