"Finding the Top 10 Most Frequently Pulled Packages from Nexus Repository" matching MCP servers:

NEXUS enhances AI coding agents with semantic code indexing, context engineering, change impact analysis, persistent memory, safe execution, and deterministic orchestration through 31 MCP tools.

Enables natural-language analysis of GitHub repositories by exposing repository metadata, source code retrieval, search, and file reading as MCP tools, with answers grounded in the actual repository content.

Enables MCP-capable agents to read, file, slice, and map a shared Workflowy outline, giving them the same live context a human sees—including source code mapped from disk—without exports or copies.

Enables coding agents to query a parsed graph of a repository and to recall what earlier sessions learned about it, both stored in FalkorDB behind one traversal. It exposes eight MCP tools over stdio for locating symbols, tracing calls and impact paths, and writing or recalling user- and repo-scoped facts, procedures, and handoffs.

Provides intelligent codebase analysis, dependency scanning, architecture detection, security vulnerability scanning, and automatic documentation generation for modern development teams.

Unified MCP server: hybrid search + code graph + semantic memory. 10 tools, <350MB RAM, fully local. No API keys.

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.

Enables AI IDEs and coding agents to navigate code at the AST symbol level, perform surgical, collision-free refactoring, and resolve live library documentation. It also supports project onboarding memory, giving agents semantic precision without regex guesswork or heavy dependencies.

A multi-purpose MCP server with 11 tools for math, text analysis, data conversion, code analysis, charts, generators (UUIDs, hashes), datetime operations, regex matching, color conversions, CSV parsing, and diff comparison.

Enables coding agents to audit source files against configurable line-count thresholds and generate modular split blueprints for oversized TypeScript, JavaScript, and Python files without destructive code changes.

Exposes 11 agent-native QA tools over stdio so coding agents can discover a project, analyze risk, select the smallest relevant test set, generate and dry-run tests, triage failures, propose tiered heals, analyze flakes, generate quality reports, and evaluate release readiness. Planning and healing tools stay read-only or propose-only, while execution defaults to dry-run, keeping agent actions safe and evidence-backed.

Enables MCP-compatible LLM clients to retrieve repository metadata, list and read files, search code, and fetch README content through natural-language requests.

MCP server that inspects Git repository changes, runs optional validation commands, and generates Markdown reports. Exposes a review_repository tool for AI clients to analyze repositories.

Enables AI agents to inspect Git repositories for changes across committed, staged, unstaged, and untracked scopes, and run allowlisted validation commands with structured results.

Provides tools for analyzing code repositories, including structure, dependencies, and git history, with special support for Nostr-related projects.

Enterprise-grade code intelligence server for AI assistants that analyzes codebases for due diligence, tech debt, security vulnerabilities, and code quality.

Analyzes GitHub and local repositories to automatically generate visual architectural diagrams such as dependency graphs, class diagrams, and data flow diagrams.

Enables AI clients to inspect Python repositories read-only through eight MCP tools that expose symbols, cited source excerpts, import dependencies, and supported static caller relationships without importing or executing the code.

Enables querying a TypeScript repository's structure (symbols, imports/exports, references) through direct queries, avoiding repetitive grep operations.

Enables AI clients to inspect Git repository changes, run allowlisted validation commands, and generate Markdown review reports via MCP, with validation disabled by default for security.

Enables large language models to explore, inspect, and analyze GitHub repositories through natural-language queries, using MCP tools to retrieve repository data, file structures, file contents, and code search results from the GitHub REST API.

Scans GitHub repositories for security vulnerabilities by cloning, performing static analysis, secret detection, build verification, and AI-powered OWASP-aligned code review, producing a scored SECURITY.md report.

Enables AI agents to inspect and reverse-engineer native binaries, JavaScript/Electron apps, Go and Rust binaries, WASM, EVM bytecode, SQLite databases, and recorded crashes through a single MCP interface that bridges tools such as Ghidra, Frida, Hopper, binwalk, and jadx. Returns evidence-backed findings—pseudocode, symbols, strings, imports, routes, IPC boundaries—so agents can explain behavior and build similar features.

Enables read-only, workspace-bound inspection of repositories and log files, returning JSON reports with file counts, largest files, TODO/FIXME markers, and warning/error counts.

Enables users to profile public GitHub repositories via a single tool, checking for Spring/Java structure such as pom.xml, src/main/java, Java file counts, and Spring layer annotations to assess suitability for deep local analysis.

Enables agents to audit and safeguard repositories by detecting dependency pinning issues, license compliance problems, hardcoded secrets, and dead code through MCP tools.

An MCP server that lets AI models run code in 31 languages, evaluate symbolic math and logic problems, and measure complexity—exposed as 48 tools for execution, session management, translation, optimization, and more.

Enables coding agents to audit a Next.js codebase read-only, checking routes, Prisma models, environment variables, and locale consistency via MCP tools.

Enables AI agents to efficiently retrieve relevant code context via PageRank-optimized subgraphs and automate spec generation, implementation planning, and pre-commit validation.

MCP server for automatic Storybook story generation, component analysis, and validation. Auto-detects React frameworks and syncs component documentation.