"An analytics platform for processing real-time data events" matching MCP servers:

An MCP server that enables AI assistants to create and run Processing sketches directly through natural language commands.

A Model Context Protocol server for time manipulation tasks, enabling AI models to get the current date/time and calculate duration between timestamps.

The Problem Modern distributed systems and AI agents require robust operational scaffolding. Simple CRUD apps or mock loops fail when subjected to real-world edge cases, asynchronous boundaries, and security constraints. The Solution An isolated execution platform for AI agents. An agent is given a broken service and a bounded toolset inside a disposable sandbox. Every command passes throug

The Fabric-Analytics-MCP server enables AI agents to interact directly with Microsoft Fabric using natural language. It transforms complex data engineering tasks—such as workspace management, data exploration, and job execution—into intuitive, conversational workflows for LLMs like Claude or GitHub

MCP server for the Agent Platform API, enabling management of sandbox environments through MCP protocol. Exposes tools to interact with sandbox lifecycle operations such as start, stop, and status checks.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

A Model Context Protocol server that enables AI models to access tools through GitHub authentication and provides comprehensive analytics tracking of tool usage, performance, and user behavior.

Local MCP server that registers restricted Python filters and runs them against local JSON, YAML, and TXT files, enabling safe data filtering and file conversion.

Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.

Enables AI agents to invoke a local Stata installation, including a Windows Stata build under Wine on Linux and macOS, for regression analysis, econometrics, paper replication, and empirical research with command guards, resource monitoring, and automatic log capture.

A Model Context Protocol server that enables Claude to load CSV files and execute JavaScript data analysis scripts for advanced data exploration and insights generation.

Enables agents to synthesize typed MCP tool schemas at runtime, validate Python ASTs for unsafe imports, compile sandboxed callable handlers, and hot-plug them into an active MCP runtime. It provides a zero-dependency, standard-library-only MCP server for safe just-in-time tool creation and execution.

Enables agents to dynamically generate typed MCP tool schemas, validate Python execution ASTs against unsafe imports, and hot-plug sandboxed callable handlers into an active runtime. This allows new tools to be synthesized and safely executed on demand.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

A modular platform that enables LLM agents to discover, register, and execute both local tools and tools from external MCP servers, with REST APIs for server management and Streamable HTTP support.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

Enables Windows users to connect Antigravity CLI as an MCP subagent for coding tasks such as file reading, code execution, edits, and terminal commands, with model selection, conversation continuity, and streaming progress.

Enables Claude Code to work with personal data that stays on the local machine, running SQL, Python, and file/schema inspection in a sandboxed compute-to-data setup that returns only aggregates, schemas, synthetic fixtures, and pseudonymized results. Raw records, file names, and protected values never reach the Anthropic API, so Auto mode can stay hands-off.

MCP server that exposes Ansible modules to AI agents with a search-schema-check-execute workflow, enabling safe ad-hoc runs via check mode by default. It provides tools to search modules, fetch schemas, run modules, and list collections.

An MCP server that grants AI agents unrestricted file system, Python, and PowerShell access on Windows for real, unfiltered automation.

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

AI-driven tool that helps developers create beautiful UI components instantly through natural language descriptions, integrating with popular IDEs like Cursor, Windsurf, and VSCode.

Enables AI agents to interact with the Roark API by writing and executing TypeScript SDK code in an isolated sandbox, with built-in documentation search to guide those tasks.

Turns any LLM into a coding co-pilot with production-ready MCP server providing lint, test, execution, generation, and graph tools. Features Neo4j persistence, OpenAI Agent integration, REST API, and React frontend for comprehensive code development assistance.

An embedded MCP server that lets AI assistants inspect and operate a running Cocos Creator project, execute JavaScript in editor or scene context, and configure supported clients with one click.

An MCP server that provides data bridging from Spring Boot interfaces and a lightweight Python sandbox for script execution. It enables agents to fetch data as Markdown or Parquet files and perform automated data analysis within a controlled environment.

An embedded MCP server for Cocos Creator that enables AI assistants to inspect and operate running Cocos projects through tools like execute_javascript, get_project_info, and screenshot capture.

A starter template for creating MCP servers that work with Puch AI, featuring ready-to-use tools for job searching and image processing. Includes examples for Bearer token authentication, OAuth integration with Google and GitHub, and demonstrates user-scoped data management.

Enables AI clients to interact with and control the Unity Editor through a Python MCP server bridge, allowing natural language-based Unity project manipulation.

A platform that transforms AI development with intelligent context management, optimization, and prompt engineering, enabling developers to enhance model performance through structured context management and optimization tools.