"Equipment or items used in windsurfing" matching MCP servers:

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

MCP server for ai-scanner that enables AI agents to scan codebases for LLM usage, AI frameworks, and exposed secrets.

Enables coding agents to discover which pinned dependencies changed after their training cutoff, and to search those changes in their own words to get matching diffs, changelog items, and upgrade notes. It also writes a short notes block into AGENTS.md or CLAUDE.md and flags outdated API usage after each edit.

Enables AI agents to navigate JS/TS, HTML, and CSS code — symbol info, outlines, callers, implementations, definitions, references, hover, and diagnostics — through bundled Node language servers. It also builds a cross-file index answering where CSS custom properties and #id/.class selectors are defined and used across stylesheets, markup, and scripts.

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

An MCP server for React Native Upgrade Helper diffs. It fetches the same generated template diffs used by React Native Upgrade Helper, backed by react-native-community/rn-diff-purge, and exposes them as MCP tools that agents can call directly.

Enables AI agents to parse Rockwell/Allen-Bradley L5X project exports and query PLC tags, UDTs, routines, AOIs, and I/O modules, including cross-reference search across logic to trace where a symbol is used. Ladder logic is returned as compact NeutralText with rung comments, keeping context windows small.

Exposes tools that let AI coding agents browse a repo's schema snapshot of tables and columns and validate their SQL against it before running or writing it, so queries referencing invented or stale table and column names get caught and corrected up front. Provides list_tables, describe_table, search_columns, and check_sql over the same snapshot used by the CLI, hook, and CI check.

Enables read-only MCP clients to list tracked projects, saved activity items, and persisted inspection overviews from Oculus.nvim's JSON state, including cached AI explanations and suggested patch locations. It does not modify Oculus state or require Neovim to be running.

Enables exhaustive, unit-by-unit analysis of open-source repositories through MCP, cataloging every file and symbol, resolving dynamically built routes and URL paths, and mapping call and value flows via LSP and Joern. Every conclusion carries file:line evidence tied to a pinned commit, with unresolved items tracked in an unverified list and assigned verification grades L0–L2.

Bridge the Java-to-Native Chasm in Android Reverse Engineering. Automated RegisterNatives Recovery, Stripped ARM64 .so Type Reconstruction, and a Unified Jadx + Ghidra or IDA MCP Server for AI-Assisted Malware Analysis.

Enables asking a consuming app in plain language to audit its design system usage, returning tables that sort every value and component by origin—design system, app-named, or none—along with token and contrast checks.

Enables agents to run static heuristic scans over a repository path via stdio, surfacing review candidates with file:line evidence for web3 auth and payment vulnerability patterns. Results can be used as CI-style gates or triage input without any dataflow analysis or external dependencies.

Enables auditing npm, Yarn, and pnpm lockfiles in local directories or remote Git repositories through MCP tools, returning vulnerability results with configurable thresholds, registry, dev-dependency skipping, and text or JSON output.

Enables agent harnesses to explore a codebase in read-only mode, create line-anchored hierarchical execution maps, retrieve them as context, and verify map drift against real code or CI.

Enables authenticated MCP clients to manage versioned research documents, work items, and immutable notes, and to run scheduled static security audits over selected GitHub repositories through a configured AI provider.

Enables AI assistants to trace which C# code, controller actions, and Razor pages reach a given SQL Server table or stored procedure in a local source tree, returning paths and confidence labels.

A wingman MCP server for Claude Code that provides auxiliary tools for file summarization, text compression, and diff review to ensure checklist items are not missed.

Gives coding agents live localization context — catalog keys, where each string is used in code (component, UI role, file:line), glossary terms and notes — so new strings can be added and translated with validated output as you write the app.

A Python MCP server that decompiles Android APK/XAPK/JAR/AAR files and extracts HTTP APIs used by the app.

Enables MCP clients to submit a repository's files and receive one checkable report of which contribution guardrails—DCO, CLA, changesets, and commit sign-off—are present, with supporting evidence and missing items.

Enables LLM coding harnesses to profile authorized repositories, select applicable defensive security techniques, and report coverage and plain-language results in markdown or SARIF while keeping scans read-only and authorization-gated.

Enables scanning a repository's source files for actual environment variable reads and diffing those names against .env.example or .env.sample. Returns a deterministic JSON report with the sync verdict, counts, and findings for used-but-undocumented, documented-but-unused, and missing-example vars, without ever reading values.

Enables AI agents to scan code for TODO, FIXME, XXX issues via MCP, providing prioritized findings in table, JSON, or SARIF format.

Gives coding agents a read-only, token-budgeted code graph spanning every repository in a project: symbols, calls, imports, and cross-repo HTTP, config, and package links, each backed by file:line evidence on both sides. This lets agents trace how symbols connect across repos and determine the impact of an API change by distinguishing links that are actually used from ones that are only declared.

Enables security auditing of MCP servers by running them in a sandbox with fake secrets, capturing outbound traffic, and detecting tool poisoning or secret exfiltration before approval.

Enables AI coding agents to quickly find relevant code, focus reads on important parts of large files, and pick relevant items from long lists, reducing token usage and search time.

Enables MCP clients to analyze a module set's exports and imports to identify exported names that are never used, returning per-module summaries, a flat dead-export list, and set-wide dead-ratio totals.

Enables local AI models to be used through MCP, exposing tools for coding assistance, code review, model listing, status checks, and benchmarking.

Enables AI coding assistants to scan an iOS, Android, Flutter, React Native or Expo project for App Store and Google Play rejection risks, returning deterministic findings that cite the guideline section, link to the official Apple or Google page and explain how to fix each problem. Nothing leaves the machine and no AI is used in the checks.

Gives AI agents controlled, full-CRUD access to real ABAP repositories through ADT on SAP ECC/S/4HANA on-premise and BTP ABAP Cloud systems, so analysis and changes are grounded in system data. Supports where-used and dependency analysis, object metadata and repository navigation, transports, and iterative creation, reading, updating, and deletion of classes, interfaces, function groups/modules, programs, DDIC, and CDS/view/service artifacts.

Analyzes C++ source code to automatically generate UML class diagrams in PlantUML format, extracting classes, inheritance relationships, and member visibility from directories or file contents.

CodeGuard MCP is a real-time AI code security scanning tool used to detect vulnerabilities, keys, and compliance issues in AI-generated code, and is suitable for code security reviews in development environments

Provides a tool to extract and validate regex patterns from text content, including flags, positions, and ReDoS risk assessment. Enables AI agents to identify potentially dangerous regular expressions in code or files without needing filesystem access.

Enables AI agents to edit files in VS Code with an interactive diff review panel, allowing users to accept or reject changes.

Enables AI assistants to pack entire repositories into XML, Markdown, JSON, or plain text, with token-reducing compression, content analysis, pattern search, remote GitHub repository support, and built-in security scanning.

Enables agents to extract hardcoded numeric values from supplied text or code across many data and source-code formats, returning values in document order with notation and available line/column positions. Supports deduplication and truncated result sets.

Enables processing documents larger than LLM context windows using symbolic commands in Nucleus DSL, executed by the Lattice engine, without chunking or vector databases.

Enables auditing npm, Yarn, and pnpm lockfiles in local project directories or remote Git repositories through an MCP tool, returning unified vulnerability results with configurable thresholds, registry, dev-dependency skipping, retries, and text or JSON output.

Enables AI agents and editors to run complexity inspections on the fly, returning compact offense JSON with rule, location, score, and vector data for any given path or inline source so oversized functions, bloated modules, and dead or single-use locals can be fixed in the same turn instead of surfacing later in CI.