The Model Context Protocol (MCP) ecosystem is exploding with innovation. Every week, new servers emerge that supercharge LLM agents with tools to operate on real-world data, code, infrastructure, and interfaces.
This 3-part series dives into the top 10 MCP servers this week. Each tool on the list enhances AI capabilities: from document conversion and codebase packaging to API gateway automation.
In Part 1, we focus on the first three servers making waves: MarkItDown, RepoMix, and APISIX-MCP.
1. MarkItDown Document Converter (Microsoft)
GitHub Activity & Adoption:
MarkItDown has garnered over 59,000 GitHub stars — a strong signal of developer demand for open, reliable document conversion tooling in the age of AI. Originally open-sourced by Microsoft, it has quickly become the de facto replacement for older tools like Pandoc or Textract in AI applications. Its MCP integration has only accelerated adoption, enabling seamless interoperability with LLM agents.
How it works in practice:
Most AI document chains break down during preprocessing. MarkItDown prevents this by extracting semantically coherent sections, cleaning up formatting noise, and preparing token-optimized Markdown in a consistent schema. The result? Faster ingestion, better chunk recall, and lower embedding cost for enterprise-grade RAG pipelines.
Getting started tip:
Pair MarkItDown with a local embedding database like ChromaDB or LanceDB. Configure your agent to convert uploads automatically using MarkItDown, then ingest and query without writing a single line of preprocessing code.
MarkItDown is Microsoft’s open-source, AI-first document processor that converts almost any file format into clean, structured Markdown — a format optimized for token efficiency and readability by large language models (LLMs).
Key capabilities:
Supports PDFs, Word, Excel, PowerPoint, images (OCR), audio (with transcription), CSV/JSON/XML, HTML, ZIPs, and YouTube URLs
Converts these inputs into Markdown while preserving document structure, lists, tables, and headings
Optimized for minimal token usage and better semantic chunking for RAG (retrieval-augmented generation)
Why it’s trending:
MarkItDown addresses a deep bottleneck in AI systems: the gap between raw user data and model-ready input. Its ability to preserve hierarchy — headers, bullet points, and tables — means LLMs can “see” and reason through documents logically. Developers on Hacker News describe it as a “modern, AI-focused take on Pandoc,” while enterprise users value its plug-and-play design for internal tooling.
It also supports multi-step workflows: convert → embed → query. With MCP integration, agents can call MarkItDown as a tool directly to fetch, parse, and deliver readable content in real time.
Market impact:
MarkItDown’s success is a bellwether for the next wave of AI development — one in which context engineering becomes a first-class discipline. Its rise illustrates the growing importance of high-fidelity data ingestion and conversion as preconditions for powerful, trustworthy LLM applications.
MarkItDown’s rise highlights how critical high-quality context conversion is in AI pipelines. It also reflects a growing need for document agents in customer support, compliance, and productivity stacks.
2. RepoMix Codebase Packager
GitHub Activity & Adoption:
With over 16,900 stars on GitHub and growing traction in open-source developer communities, RepoMix has struck a nerve in the LLM and DevOps ecosystem. It has been nominated for open-source innovation awards and is frequently recommended by AI toolchains looking to scale across monorepos. The addition of MCP server support marks its evolution from a static CLI utility into a dynamic agent-native tool.
How it works in practice:
RepoMix produces a JSON manifest that includes file-level tokens, a structural overview, and a zipped payload. You can feed this into a multi-step agent that first scans dependency graphs, then performs module-level QA or transformation. It’s ideal for teams refactoring legacy code or onboarding junior devs with AI walkthroughs.
Getting started tip:
Start with repomix bundle --source ./your-project/ --exclude tests and connect the resulting archive to a Claude or GPT-based repo explainer agent. You’ll immediately reduce context errors and improve traceability.
RepoMix solves a fundamental problem in AI code assistance: giving LLMs structured access to an entire codebase. Instead of manually piping in one file at a time, RepoMix bundles a project into an AI-readable archive — complete with token estimates, metadata, and optional tree-sitter compression.
Key capabilities:
One-command packaging of codebases into searchable, memory-efficient documents
Skips .gitignored files and scans for secrets via Secretlint
Tracks per-file and total token usage, allowing tuning for context windows
Includes experimental features like code chunking and dependency graphs
Why it’s trending:
AI tools are being used across the SDLC: for code review, architecture mapping, onboarding, and even automated refactoring. RepoMix makes those workflows scalable by abstracting away the painful I/O problem of large repos. Developers have called it a “bridge between LLMs and real-world codebases,” particularly in complex CI/CD pipelines and containerized environments.
Combined with MCP, agents can now fetch summaries, retrieve modules, or explain repo-level concepts in seconds. The tool’s simplicity — “just one command to pack your repo” — is a major part of its appeal.
Market impact:
This tool represents the shift from file-level to project-level AI understanding. Expect RepoMix-style bundlers to become standard in IDEs and build pipelines that include AI linting, gen-tests, or doc-gen agents. Its emphasis on token-aware packaging, secure file filtering, and multi-agent support hints at a broader trend: AI copilots are no longer coding assistants — they’re becoming system-level interpreters. RepoMix is one of the clearest signs of that transition.
This tool represents the shift from file-level to project-level AI understanding. Expect RepoMix-style bundlers to become standard in IDEs and build pipelines that include AI linting, gen-tests, or doc-gen agents.
3. Apache APISIX Gateway (MCP Integration)
GitHub Activity & Release History:
Apache APISIX, with over 15,000 GitHub stars, launched its MCP plugin in April 2025 — making it one of the earliest production-grade infrastructure tools to integrate AI-native interfaces. The plugin is available via NPM and GitHub, and includes both Admin API bindings and a flexible mcp-bridge module for hosting any stdio-based MCP server behind a secure API gateway. This initiative aligns with APISIX’s vision to become the “AI Gateway” of the future.
How it works in practice:
Using APISIX-MCP, AI agents can query existing route configs, test plugin compatibility, and simulate deployment updates in dry-run mode. This drastically reduces the risk of production misconfigurations — especially in zero-downtime environments. Teams have used it to roll back faulty routes, enable plugins during incident response, and validate staging updates — all through natural language prompts.
Getting started tip:
Use the built-in mcp bridge plugin to expose legacy shell tools over HTTP. Then, teach your agent to discover and trigger these routes contextually — no dashboard or manual intervention required.
Apache APISIX, a high-performance API gateway used by enterprises globally, now offers native support for AI integration through its MCP plugin. This allows AI agents to query and modify gateway configurations like routes, services, and plugins via natural language.
Key capabilities:
Full CRUD access to Admin API endpoints via secure tool calls
Real-time route creation, service deployment, plugin toggling
Can expose stdio-based MCP tools via HTTP using APISIX’s mcp-bridge plugin
Ideal for hybrid cloud DevOps workflows
Why it’s trending:
AI in infrastructure is no longer theoretical — it’s operational. With APISIX-MCP, teams can prototype, test, and ship API changes without touching dashboards or YAML files. It's GitOps meets ChatOps. More importantly, APISIX positions MCP as the “USB-C of AI integrations” — a clean, composable protocol that lets LLM agents plug into real backend systems.
Market impact:
APISIX-MCP signals that mature, production-grade infrastructure tools are betting on AI interfaces. In a future where “configure API gateway” is a voice or text command, APISIX wants to be the first responder. This move is part of a broader shift toward AI Ops: automating infrastructure not just with scripts, but with semantic intent. Expect to see more platforms adopt similar patterns — blending secure access, event streaming, and agent-native interfaces into their operational DNA.
APISIX-MCP signals that mature, production-grade infrastructure tools are betting on AI interfaces. In a future where “configure API gateway” is a voice or text command, APISIX wants to be the first responder.
Common Patterns Among the Top 3 Servers
Together, MarkItDown, RepoMix, and APISIX-MCP reveal a clear pattern in where the MCP ecosystem is heading:
Token-efficient context engineering is now a priority. Whether it’s documents or code, the trend is toward highly structured, compressed input formats.
AI-readiness is being baked into infra tooling. APISIX’s integration shows that leading open-source platforms are racing to become agent-accessible by default.
Multi-modal pipelines are becoming standard. Agents need to read, write, and orchestrate across formats — from Markdown to Git trees to HTTP routes.
Sample Use Cases in the Wild
Case 1: AI-Supported Document Reviews in Finance
A compliance officer uploads a PDF contract into a local folder. An agent uses MarkItDown to convert and parse key clauses, flags unusual language, and summarizes risk areas. All via natural language query.
Case 2: Project Refactor Using RepoMix + Claude
A dev team preparing for a monorepo migration uses RepoMix to bundle legacy services. An agent processes each module, builds architecture diagrams, and recommends breaking changes — saving days of manual effort.
Case 3: ChatOps for Microservices
During an incident, a team uses an LLM interface integrated with APISIX-MCP to roll back an API change and deploy a plugin patch — with audit trail and no manual CLI commands.
These aren't sci-fi prototypes. They're active agent workflows emerging today — powered by the rising stars on this week’s MCP leaderboard.
What’s Next?
In Part 2, we’ll explore four more rising stars in the MCP server space — including agents that can watch your screen, navigate the web, and automate GitHub workflows. These tools are expanding the sensory and operational range of AI assistants.
Stay tuned for weekly updates on the fastest-growing tools in the MCP ecosystem, only on the MCP Now blog – your source for agent infrastructure, use cases, and deep technical insights.
Ready for more?
Read Part 2 to explore MCP servers powering browser automation, desktop capture, and natural language interfaces.
Or jump to Part 3 to see how AI agents connect with documentation, 3D software, and cloud storage.