"Current Weather Information or Forecasts" matching MCP servers:

Review and rewrite LangChain, LlamaIndex, Ollama, and XRPL code against current APIs, plus runnable examples, import lookup, and XRPL tx skeletons that never submit. Live docs included. $0.001 USDC on Base or 1000 drops XRP.

Provides access to Ant Design component library documentation and information for automated code generation. Enables searching components by category or keyword, retrieving component props, examples, API documentation, and available icons.

Enables local detection of protected health information in source code before it reaches LLM prompts, log statements, or analytics calls.

Checks mocks, stubs, and spies against current production code to detect stale or mismatched test doubles without running any tests.

MCP server that exposes actionable Omniboard checks for the current project to a local agent.

Enables cleaning and sanitizing prompts through an LLM-powered tool that removes sensitive information, provides structured feedback with notes and risks, and normalizes prompt formatting. Supports configurable local or remote OpenAI-compatible APIs with automatic secret redaction.

Provides project context for AI agents in VS Code by analyzing technologies, structure, AGENTS.md rules, current branch, and source code without allowing arbitrary commands.

Enables AI agents to analyze large codebases and document collections by consulting large context window models (up to 2M tokens) via OpenRouter when files exceed the current agent's context limits.

An MCP server that enables AI assistants to parse Xcode and Swift build outputs into structured, token-efficient formats like JSON or TOON. It provides tools for executing build commands and extracting detailed diagnostic information such as errors, warnings, and test failures.

Enables AI coding agents to inspect Runable projects over MCP, providing read-only access to project information and configuration via Runable's inspector.

Enables coding agents to generate question-scoped repository maps, inspect symbols and SQL lineage, and audit working-tree diffs for ripple candidates, all verified against the current Git commit.

Enables AI agents to carry durable project memory across windows, agents, and days, injecting a compressed context of architecture, progress, decisions, and critical files so a new session can resume without re-reading the whole repository. It also classifies files as current/legacy/intermediate and suggests cleanup so stale or draft artifacts don't mislead the agent.

Enables intelligent Go code quality checking with smart change detection through golangci-lint integration. Automatically detects branch development scope and focuses on current changes to avoid historical code issues.

Give your coding agent the repo context it needs before it starts searching. Madar builds a local graph of your TypeScript or Node.js repository and turns the current question into a small, task-aware context pack. Claude Code, Codex, Cursor, Copilot, Gemini, Aider, and OpenCode can start from relevant files, symbols, snippets, and relationships instead of rediscovering the repository from scratch

Enables agents to query information about gems in a Ruby project's Gemfile, including source code and metadata.

Access your live RStudio session from any MCP-capable client. Inspect objects, preview data, and execute R code directly in the current RStudio environment.

Enables AI assistants to analyze project folders for dependencies and frameworks and fetch current documentation or datasheets from the web.

Enables querying any GitHub, GitLab, or Bitbucket repository and getting AI-generated answers about the codebase, powered by Gemini.

Twelve tools combine per-function complexity and branch coverage to rank work and check edited functions against their ceiling. Packets include source, uncovered lines, change-coupled files and near-duplicates. MCP calls can write local caches and metadata; they do not run test suites, edit source or claim work.

An MCP server that scans local codebases for quantum-vulnerable cryptography (secp256k1, Ed25519, RSA, etc.) and CI signing commands, classifying each finding as quantum-broken, post-quantum, or neither. It runs entirely locally with no network calls, providing a deterministic inventory for AI agents.