Search Results for "hpc code analysis"

The DeepSource MCP Server enables AI assistants to interact with DeepSource's code quality analysis capabilities through the Model Context Protocol. It allows retrieval of code metrics, access to issues, quality status checks, and analysis of project quality over time. ​

The SonarQube MCP Server is a Rust implementation that integrates SonarQube's code quality analysis with AI assistants through the Model Context Protocol. It provides access to code metrics, issues, quality gate statuses, and project quality analysis. ​

The IDA Pro MCP Server is a Model Context Protocol (MCP) server designed to integrate with IDA Pro, a popular disassembler and debugger. It enables AI assistants to interact with IDA Pro, facilitating tasks such as code analysis and reverse engineering. ​

Codex MCP Server is an open-source integration tool that allows AI development environments to access the capabilities of the OpenAI Codex command-line interface through the Model Context Protocol. The project acts as a bridge between AI assistants such as Claude Code and the Codex CLI, enabling those assistants to perform advanced coding operations using Codex as a backend engine. Through this architecture, developers can request tasks such as code explanation, refactoring, or analysis directly from their AI assistant while the server forwards the request to Codex. The system manages communication between the assistant and the Codex CLI, handling sessions, command execution, and structured responses. ...

UltraRAG 2.0 is a low-code, MCP-enabled RAG framework that aims to lower the barrier to building complex retrieval pipelines for research and production. It provides end-to-end recipes—from encoding and indexing corpora to deploying retrievers and LLMs—so users can reproduce baselines and iterate rapidly. The toolkit comes with built-in support for popular RAG datasets, large corpora, and canonical baselines, plus documentation that walks from “quick start” to debugging and case analysis.

Colab-MCP is an open-source Model Context Protocol server developed by Google that enables AI agents to directly interact with and control Google Colab environments programmatically, transforming Colab into a fully automated, agent-accessible workspace. Instead of relying on manual notebook usage, the system allows MCP-compatible agents to execute code, manage files, install dependencies, and orchestrate entire development workflows within Colab’s cloud infrastructure. This approach bridges the gap between local AI agents and remote high-performance compute environments, allowing users to offload heavy workloads such as machine learning training, data analysis, and dependency-heavy tasks to Colab’s GPU and TPU resources. ...

mcp-server-chart is a TypeScript Model Context Protocol (MCP) server that turns AntV’s visualization stack into agent-callable tools for automatic chart generation and lightweight data analysis. Out of the box it exposes more than 20–25 chart generators—covering staples like bar, line, area, histogram and pie, plus advanced visuals such as dual-axes, heatmaps, radar, flow and fishbone diagrams—so an AI client can request a chart and receive an image URL in return. The server can run over stdio for desktop IDEs or via SSE/“streamable” HTTP transport, making it easy to plug into MCP-capable clients and platforms (including Dify) without custom glue code.

FlowLens MCP Server is an open-source tool designed to give AI-powered coding agents (like Claude Code, Cursor, GitHub Copilot / Codex, and others) full, replayable browser context to dramatically improve debugging, bug reporting, and regression testing for web applications. It works together with a companion browser extension: when a user reproduces a bug or a complicated UI interaction, the extension captures a rich session log, including screen/video recording, network traffic, console...