"Top trending MCP servers currently popular" matching MCP servers:

Lets AI coding agents read an outline of a large file or a digest of noisy command output instead of the full text, while every hidden line stays retrievable on disk through a run handle. Agents can request only the line ranges, slices, or grep matches they need and list recent runs, cutting token spend without losing information.

Offers a control surface for AI agent security with four independent checks: source-code scanning, tenant isolation, LLM content verification, and deploy gating. Currently in pre-release, with no functional scanning engines yet.

Enables AI coding agents to search a security corpus, scan Terraform plan JSON for cited findings, and list sources via MCP tools grounded in CIS AWS Foundations and OWASP Top 10 CI/CD risks.

MCP server for exploring GitHub public data. It provides tools to fetch user profiles, list top repositories, and calculate language usage statistics, which AI agents can invoke dynamically.

A local-first MCP preflight system to help AI inspect Python repositories before generating code, enabling reuse, extend, or create decisions. Currently in early development with only foundational modules implemented.

Reduces Claude's context window costs by automatically summarizing inactive files to their public interfaces using AST parsing, keeping only the full contents of the currently active file.

Security scanning for MCP servers from the inside out. Provides runtime inspection, AST-based static analysis, config audit, dependency analysis, and OWASP MCP Top 10 compliance in a single MCP server.

MCP server that scans Salesforce Agentforce metadata for security vulnerabilities using 61+ SAST rules, integrating into AI coding workflows to guard against OWASP LLM top 10 risks.

Enables Codex to retrieve the currently selected file, line numbers, and code snippets from JetBrains IDEs. It leverages the official Claude Code plugin to provide real-time IDE context to the AI.

Tracks latest versions and breaking changes for popular SDKs, enabling agents to check version updates and potential breaking changes efficiently.

Exposes boyter/scc code counting and complexity analysis to LLM agents via read-only tools like counting lines, finding top files, and cost estimation.

Semantic code search MCP server that reduces token usage by ~95% by returning top relevant code chunks instead of full files.

This MCP server assists Minecraft Java plugin developers by providing dependency documentation lookups, version checking, and automated scanning for Gradle and Maven projects. It supports popular APIs such as Paper, Spigot, and LuckPerms to streamline plugin development and workspace analysis.

Enterprise-grade zero-dependency security shield and runtime governance engine for MCP servers, providing OWASP MCP Top 10 audits, cryptographic tool call signing, AIBOM generation, and inline parameter firewall protection.

An MCP server for C++ development that currently provides a server_status diagnostic tool. It validates a workspace directory and is designed to expand into build, test, and debugging capabilities.

A multi-language code analysis server that helps LLMs or humans automatically lint, type-check, and improve code with minimal installation friction, currently supporting Python with plans for other languages.

AI-powered git diff analysis with human-readable explanations, risk flags, and review checklists. Enables to explain any diff text or currently staged git changes through an MCP server.

Enables AI tools to read, edit, check, and export the currently open Figma file locally, with support for design files, FigJam boards, and Slides decks plus quality checks and design-to-code workflows.

Run Semgrep static analysis from an AI agent. OWASP top 10, secrets detection, custom rule packs, baseline scanning. Curated by Archimedes Market with a verified Trust Report.

Enables intent-driven version control on top of Git, turning commits into auditable intents with prompts, AST deltas, and signatures, and providing tools for blame, context, replay, and verification.