"Top 100 MCP Servers Ranked by Weekly Downloads" 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.

AgentAvow scans any MCP server, package (npm/PyPI/crates/Docker/Hugging Face), or GitHub repo and returns a signed, offline-recomputable 0–100 trust score with a plain safe / needs-review verdict. Authless and read-only, no account.

Provides coding agents with file inventory, ranked search, location-aware reading, code packing, and note writing over the workspace folder through MCP, without involving a model.

Enables AI agents to debug errors by providing root cause analysis and ranked fixes with code patches. Supports any MCP client.

Enables AI agents to perform semantic code search across entire codebases using natural language queries. Provides fast indexing and ranked search results with line numbers and file paths through the Seroost search engine.

Assesses code change risk with ultra-low token budget (50-100 tokens) and provides structured risk metadata for AI agents in IDEs.

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.

A stdio MCP server that audits other MCP servers by linting their tool schemas and calling tools with malformed inputs to produce a 0–100 conformance score and Markdown report.

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.

Security scanning for AI agents: SAST, SCA, secrets, IaC, DAST, ranked by real risk.

Enables auditing a repository's Claude Code setup, scoring it from 0 to 100, and scaffolding agents and hooks to fix issues.

Enables AI agents to query Linux kernel source code, git history, LKML discussions, Patchwork patches, and documentation with evidence-ranked results, citations, and explicit assumptions or contradictions.

Enables AI coding agents to query a compiled typed symbol graph over MCP, revealing real callers, callees, blast radius, data flow, constraints, dead code, and commit-history signals before editing. Supports repo syncing and ranked context retrieval so agents can avoid structural regressions.

Turn any .NET test-automation solution into a queryable map in one SQLite file — features, steps, API clients, page objects, and their dependencies. Zero config, no AI, no network, 100% deterministic.

Enables any AI coding agent to search huge codebases cheaply by sending symptom-based locate, classify, and typed decision questions to OpenRouter decision models, which return ranked file:line locations, confidence scores, and the relevant code instead of the whole repository. It supports model selection, context-aware chunking, and fallbacks so agents can pinpoint bugs or filter files for a fraction of a cent.

Provides software supply-chain intelligence for AI agents, enabling them to query package metadata, versions, downloads, dependencies, and health signals for npm, PyPI, and crates.io packages without API keys.

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.

An MCP server that generates ranked, token-budgeted code structure maps using Tree-sitter AST analysis and PageRank, enabling AI agents to quickly understand unfamiliar codebases.

Enables AI agents to index local codebases into isolated SQLite FTS5 databases and search them for ranked file paths, line ranges, and highlighted snippets, reducing context spent opening whole files.

Enables AI coding agents and IDEs to perform AST-backed semantic Verilog/SystemVerilog code reviews, catching race conditions, reset polarity mismatches, and bitwidth truncations while computing a 0–100 RTL quality score.

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

Enables AI coding assistants to run pre-publish security and code-quality scans across 17 categories, generate a 0–100 security score and Markdown report, quickly check critical vulnerabilities, and receive fix/refactor suggestions by finding ID.

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

Analyzes Git commit history and code diffs to auto-remember development context, providing commit timelines, work sessions, unfinished task detection, and daily/weekly reports.

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.

Enables intelligent code analysis and search across repositories using the CodeRank algorithm (inspired by PageRank) to identify critical modules, trace dependencies, find code hotspots, and perform context-aware keyword searches with importance-ranked results.

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

Local-first codebase context engine that parses code into a ranked dependency graph and serves it to AI tools via MCP for deep structural understanding.

Enables AI coding agents to build and query local repository memory, returning ranked files, context packs, relationship maps, semantic recall, and feedback-tuned search without uploading code.