"KiCad MCP (Model Context Protocol) integration" matching MCP servers:

KiCad Workbench is an MCP server that lets an AI agent work with KiCad projects. The agent reads schematics, boards and libraries, runs the checks of KiCad, makes the bill of materials (BOM), and writes changes to the schematic. It supports KiCad 8, 9 and 10.

Enables AI assistants to drive KiCAD PCB design automation through MCP, covering schematic authoring, board and routing operations, footprint/symbol libraries, vendor PCB import, validation and DRC, and manufacturing export. It exposes 236 registered tools with keyword discovery, dynamic project-state resources, and optional Streamable HTTP transport with fail-closed Bearer authentication.

Conformance test harness for Model Context Protocol servers, validating JSON-RPC, transport, capability, schema, and more across multiple spec versions.

Audits KiCad PCB designs against South-East Asian fab capabilities using an IPC-compatible DFM rules engine with optional AI review via Ollama.

Enables AI agents to create and edit real KiCad schematics and projects, run ERC/DRC validation, generate BOMs, and produce schematic/PCB/3D renders via kicad-cli, with SKiDL support and automatic backups.

Enables inspecting ONNX models, probing the deployment environment, and benchmarking inference latency and FPS either locally or on remote SSH targets such as Linux workstations and Jetson devices. Benchmark results are validated against FPS, P95 latency and model-size constraints to produce PASS/FAIL deployment gates.

Enables AI coding assistants to inspect GitHub Actions CI runs, diagnose failures with failed-step summaries and filtered job logs, read per-job coverage, and view pull request checks through read-only, allowlisted access.

Enables coding agents to run budgeted tests and checks under a countdown with a stop rule, shape long logs into concise failure summaries, and guard pre-existing test files against silent assertion rewrites. It also delivers relevance-filtered project memory and renders a token-and-time roadmap of the orchestrator and every subagent spawned for a task.

Enables AI coding agents to perform zero-token static audits, simulate refactorings in an ephemeral RAM clone, trace downstream ripple effects, and block unsafe writes with automatic rollback to prevent regressions.

Enables AI assistants to generate and statically verify IEC 61131-3 Structured Text logic, mutate .csp/.cpj OLE2 project containers, trigger in-GUI Error Check compilation, manage HMI screens, and run deterministic OCS scan-cycle simulations and Modbus telemetry scaling against Cscape 10.2. All physical hardware and firmware-download paths are intercepted fail-closed, so commissioning must be performed manually by authorized field personnel.

A local MCP server that lets Claude Code and Codex delegate repository exploration and test proposals to a remote LM Studio model, while enforcing security boundaries by keeping all repository access read-only and never applying patches or running commands remotely.

Enables coding agents to capture deterministic Mozilla rr recordings and replay them through a persistent debugger session, inspecting stacks, locals, threads, and event/tick positions to trace bugs back to their root cause. Provides a compact evidence-oriented tool loop for moving forward and backward, setting breakpoints/watchpoints, and evaluating expressions during AI-assisted debugging.

Enables running the same prompt(s) across roughly 300 models from Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek, Qwen and others, then comparing per-model outputs, latency, token usage, error rates and billed cost side by side. Supports free cost estimates before launching, asynchronous run creation with status polling and cancellation, prepaid x402 credit top-ups, and an optional AI-written comparison summary of the results.

Enables MCP-compatible clients to discover API endpoints from OpenAPI/Swagger specs, send HTTP requests, validate responses, and analyze status codes for automated API testing.

Enables running deterministic multi-model prompt regression and golden dataset benchmark suites, exposing structured results and telemetry through the Model Context Protocol for integration with MCP-compliant clients.

Enables AI assistants to lint, render, fix, inspect, and find free space in KiCad schematics and PCBs, providing visual feedback and safe automated text fixes.

ModelSafetyMCP is an MCP server for scanning machine learning model artifacts for unsafe serialization, malicious model patterns, risky packaging, URL-based artifact scanning, and directory-level triage.

Enables AI agents to fetch daily deterministic constraint challenges and submit JSON answers for immediate, stateless evaluation via MCP.

Validates governance evidence for Codex development tasks, enforcing requirements alignment, single-active-subject candidates, executable specifications, and independent gates for traceable review packets.

Enables any MCP client to debug programs through the Debug Adapter Protocol, supporting launch/attach, breakpoints, stepping, variable inspection, evaluation, and memory operations via a single debug tool.

A server implementation of the Model Context Protocol (MCP) for managing development workflow with features like project management, task tracking, and QA review support.

Enables intelligent analysis of regression test failures and automatic discovery of solutions in JIRA. Analyzes test logs using AI-driven algorithms and matches errors with relevant JIRA issues through natural language interactions.

Enables learning and integration testing of the Model Context Protocol by evolving from a minimal server toward production-grade tools, resources, prompts, and agent interoperability.

Exposes an agent-based simulation of post-scarcity governance as callable tools for AI agents, including simulation runs, compliance checks, and test execution.

Enables debugging of Electron applications through the Model Context Protocol, with deep integration of Chrome DevTools Protocol for process management and advanced debugging capabilities.

A Model Context Protocol server that provides integration with TestRail, allowing AI assistants to interact with TestRail projects, test cases, test runs, and results.

A standalone server enabling Snyk security scanning through the Model Context Protocol, with support for repository and project analysis, token verification, and CLI integration.

A Model Context Protocol (MCP) server for debugging and operating Rust Tauri applications over WebSocket.

Free MCP tools for context engineering audits of AI agents, offering on-demand analysis of system prompts, tool definitions, and context packing.

Agent-readiness scorecard for any MCP server: protocol checks, 0-100 score and actionable findings.

A Model Context Protocol (MCP) server for interacting with SonarQube code quality platform.

AI-powered QA testing platform — catches JS errors, accessibility failures, visual regressions, API loops, security issues, and more via Chrome DevTools MCP

Provides instant GDScript syntax validation and diagnostics by bridging Godot's native Language Server Protocol to MCP clients. Enables real-time syntax checking in AI assistants without requiring custom plugins or context switching to the Godot editor.

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.

On-demand micro-mutation sandbox for AI test verification that maps weaknesses in unit tests by running isolated mutation testing via the Model Context Protocol.

A lightweight local MCP server that runs axe-core accessibility audits via Playwright and returns compressed, actionable results optimized for Claude's context window.

Deterministic fluff detector for AI-generated prose. No model, no API key.

A read-only Model Context Protocol server that converts local Git changes and JUnit XML reports into traceable release evidence, risk findings, and a rollback checklist.

An MCP server that enables AI assistants to control GDB debugging sessions, including breakpoint management, thread analysis, and variable inspection, using the GDB/MI protocol.

AI-optimized Vitest interface that provides structured test output, visual debugging context, and intelligent coverage analysis for more effective AI assistance with testing.