"Sort Content by Number of Downloads" matching MCP servers:

Enables checking project files like Dockerfile, .nvmrc, and CI workflows to determine whether pinned runtime, database, and OS versions are still supported, providing end-of-life dates, latest patches, and upgrade targets for 470+ products.

Enables AI assistants and developers to analyze code for language-specific best practices and idiomatic patterns across programming languages, CI automation, and configuration formats.

An MCP server that extends AI coding assistants with deterministic, algorithmic capabilities such as code analysis, fault localization, and formal verification, enabling an autonomous engineering team within the IDE.

Validates Mermaid diagrams with comprehensive grammar parsing supporting 28+ diagram types. Processes markdown files, ZIP archives, and direct input with detailed error reporting and enterprise-grade performance capabilities.

Enables MCP clients to inspect, edit, and validate Arma Reforger addons in Workbench, including Enforce Script source, prefab/world files, resource GUIDs, and Workbench startup diagnostics.

Enables static security audit of Solidity smart contracts by analyzing source code or deployed bytecode for vulnerabilities, providing risk scores and detailed findings.

Signalint is an MCP server that provides JavaScript and TypeScript diagnostics by running Oxlint, TypeScript, and optionally Biome, with caching, clustering of repeated issues, and detection of oscillating diagnostics.

An AST-based MCP server that provides token-efficient codebase skeletons to LLM agents, reducing context token usage by 80-95% by exposing structural information instead of full source files.

Provides AI assistants with secure, read-only access to ShadowGit repositories for debugging and code analysis. Enables powerful analysis of fine-grained git history captured automatically by ShadowGit's continuous commit tracking.

Enables static analysis of scripts without executing them, providing plain-English behavior explanations, decoded obfuscated content, IOC extraction, risk scoring, and MITRE ATT&CK mapping.

Provides AI coding agents with durable architecture memory for repositories by generating structured project maps of responsibilities, relationships, and risks.

A security filter that blocks dangerous code patterns by comparing normalized structural syntax trees against a blacklist of known threats using vector embeddings. It acts as a gatekeeper to prevent malicious code execution by identifying dangerous structures regardless of specific identifiers or literals.

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

MCP server for npm — search packages, check bundle sizes, scan vulnerabilities, compare downloads, inspect dependencies.

Enables evidence-driven debugging of RTL testbench simulation failures by parsing logs and analyzing FSDB/VCD waveforms through a guided MCP workflow.

Enables extraction of mathematical content from TeX papers and conversion to Lean code through a structured intermediate representation. Supports project scaffolding, entity management, and task tracking for mathematical formalization workflows.

Enables AI assistants to perform code reviews by providing access to staged files, git diffs, and repository file content. It allows users to evaluate changes and context within any local git repository before committing or pushing.

Enables AI tools to analyze Java dependencies by scanning Maven projects, decompiling JAR files, and extracting detailed class information including methods, fields, and inheritance relationships. Solves the problem of AI hallucinations when generating code that calls external dependencies by providing accurate class structures through decompilation.

Enables analysis and querying of Git repository content (both public and private) through a unified tool that provides repository summaries, file structures, and full content optimized for LLM consumption.

Enables Claude Code to query codebase knowledge graphs directly, reducing token usage 5x–71x by reading a compact graph.json instead of raw files.