"A visualization rendering service for MCP" matching MCP servers:

An MCP server that turns AI assistants into competent Festo/CODESYS PLC engineers by grounding them in curated, manufacturer-accurate knowledge and forcing every artifact through a machine-checked validation pipeline for CODESYS import.

A simple MCP server that provides a basic greeting tool and serves as a starter template for AWS Lambda deployment. Demonstrates how to build and deploy MCP servers with both local development and cloud deployment capabilities.

Educational MCP server with 17 tools for mathematical calculations, matrix operations, statistical analysis, and data visualization. Includes persistent workspace, cloud hosting, and safety features like restricted eval.

MCP server for retrieving FAQ and glossary from the hotline.finance insurance service, supporting both stdio and HTTP modes for integration with clients like Cursor, Claude Desktop, and ChatGPT.

An intelligence layer for discovering and orchestrating Japanese SaaS MCP tools through intent-based search and workflow recipes. It enables AI agents to evaluate service quality using community-driven insights while ensuring privacy through automated PII masking.

A unified MCP server that aggregates 32 sports API providers into a single service, providing 336 tools for scores, stats, odds, esports, and more across 70+ sports.

Enables integration with the Ethora platform through user authentication, registration, and application management operations. Supports creating, updating, deleting, and listing applications within the Ethora service.

A task management MCP server for AI-driven development, enabling creation, tracking, and organization of tasks with subtasks, priorities, and dependencies via natural language commands.

Provides a reference MCP server that implements every MCP feature so client, gateway, and runtime authors can test against a single endpoint, and publishes a service contract describing its capabilities.

Provides offline search and retrieval of curated scientific computing Agent Skills for modeling, computational chemistry, DFT, molecular dynamics, and visualization. It exposes tools for skill cards, compatibility checks, content/bundle retrieval, version diffs, health checks, and issue reporting via HTTP or stdio MCP.

Integrates front-end development servers with AI tools, enabling browser automation, console log capture, screenshot management, and HMR event monitoring.

An MCP server that enables AI assistants to directly control QGIS for tasks like layer management, feature editing, and map rendering. It provides a suite of 50 tools to execute processing algorithms and manage GIS projects through natural language commands.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

An MCP server that provides AI clients with access to developer documentation via llms.txt files. Exposes tools, resources, and prompts.

A local-first, no-API-key MCP server that enables LLMs to search the web, fetch pages, and read documents using multiple engines and smart fallbacks.

Enables AI assistants to perform Google searches with real, resolvable sources and transparent search terms.

MCP server for overseer.nvim — gives any MCP-capable coding agent running inside Neovim (Claude Code, Codex, Gemini CLI, opencode) the same visibility and control over tasks the user has: list, run, tail, restart, stop. Agent-started dev servers land in the user's own task list instead of becoming invisible orphaned processes.

Enables AI models to access and search offline Wikipedia and other knowledge bases stored in ZIM format files. Provides intelligent content retrieval, structured browsing, advanced search capabilities, and metadata extraction for comprehensive offline knowledge access.

Enables multi-level reasoning with configurable depth levels, session-based state management, structured thought input, and real-time trace resources.

Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.