"Bash shell scripting and command line resources" matching MCP servers:

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Enables AI agents to verify Ceph CLI commands, config parameters, and review test scripts using a pre-generated knowledge base.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Windows桌面截图与窗口自动化工具，支持后台操作、不抢焦点，可截取窗口/屏幕、模拟鼠标键盘、点击菜单等。

Provides a framework for running multiple MCP servers using FastAPI, stdio, and streamable-http transports, with an echo tool for testing and integration with MCP Inspector.

Maps Electron app IPC handlers to MCP tools and provides 33 built-in CDP tools for DOM automation, screenshots, interaction, and debugging, enabling Claude Code to directly control your Electron app.

A production-oriented reliability, benchmarking, and evaluation system that layers multi-version protocol negotiation, adaptive streaming, connection pooling, and resilience onto an MCP agent runtime.

Read-only MCP view of a repository's CRAP scores: cyclomatic complexity joined to the branch coverage the repo's own test command writes. Nine tools hand an agent the worst function to fix next with its source, uncovered lines, change-coupled files and near-duplicates.

Audits any site's UX the way a design-literate reviewer would — contrast, tap targets, type scale, colour discipline, scan patterns, copy — and returns the rule, the source line and the exact fix.

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 agents to verify completion by cold-running an acceptance command and reporting whether it exits with code 0, returning a pass/fail receipt with output and refusal reasons.

Exposes MAID Runner validation tools (validate, snapshot, test) as MCP tools, resources, and prompts for AI agents like Claude Code and Aider, enabling seamless integration with AI development workflows.

Eyes for AI coding agents: deterministic MCP server that tells the LLM which CSS rule wins, in which file, on which line — and why. Cascade verdicts, blast radius, interaction timelines, pixel-perfect audits.

Enables LLM agents to programmatically inspect, debug, and test other MCP servers by wrapping the MCP Inspector CLI. Supports listing and calling tools, reading resources, and testing prompts on both local and remote MCP servers.

Verifies AI outputs in real-time across 6 dimensions, with automatic retry and failover to ensure correct, complete, and reliable LLM responses before they reach the user's editor.

Drives a browser - or a native Expo/React Native app - and your dev server, putting both on one correlated timeline so a console error and the backend stack trace from the same moment line up. For Claude Code and AI agents.

A high-performance, multi-protocol API mock server with 18 built-in MCP tools for managing HTTP, GraphQL, gRPC, WebSocket, MQTT, SSE, and SOAP mocks, chaos engineering, stateful CRUD resources, and request verification.

Connects AI coding assistants to TestCollab for managing test cases, plans, and suites directly through natural language. It enables users to create, update, and query testing resources within integrated development environments and AI chat clients.

An independent review layer for AI-generated text: a 10-lens gate run by a separate model over output it did not write, returning the flagged line and the reason for each check rather than a single opaque verdict. A check that cannot complete reports unknown instead of a pass; lens_health needs no API key.

A specialized testing harness that enables AI assistants to thoroughly test other MCP servers by connecting to them, discovering their tools/resources/prompts, executing test calls, and performing end-to-end validation with LLM integration.