The democratization of Model Context Protocol (MCP) tools shifts power from developer-centric stacks to everyday creators, but it also introduces new friction regarding data provenance and platform policy enforcement.

Super Agents for Everyone: Choose from 100+ MCP Tools, with Stunning Xiaohongshu Scraping Results

The agent track is bustling with activity. What makes the experience of Nano AI Search, heavily promoted by Zhou Hongyi, any different?

First, it remains incredibly “crowded,” and a slight misstep can easily crash the servers.

However, after conducting further hands-on tests, we found that Nano AI Search, in terms of both its access method and product features, is distinctly “Old Zhou” (Zhou Hongyi’s style)…

Here are the conclusions:

First, it has significantly lowered the barrier to entry for using MCP. As the first truly consumer-facing (toC) MCP platform, ordinary users can now genuinely experience advanced agents based on MCP. Previously, MCP was primarily aimed at professionals and gained popularity among developers. Now, Nano AI’s 400 million users can call upon a vast array of MCP tools to complete complex real-world tasks.

Second, it represents a truly open MCP ecosystem. Nano AI currently boasts over 100 self-developed and curated MCP tools, with more third-party MCP tools in the process of joining.

Furthermore, one can see the continuation of 360’s technical advantages and product style: opting for local deployment and MCP tool integration rather than the typical cloud-hosted model. This makes it easier to bypass login walls and ad barriers during deep model retrieval and social media operations, ensuring convenience without compromising security concerns.

Let’s delve into the specific details.

Hands-on Test of Nano AI’s Universal Toolbox

I watched the creative stack shift again as Nano AI unveiled its “Universal Toolbox,” a move that prioritizes open access over walled gardens. While other platforms lock their MCP tools behind closed ecosystems, Nano AI is betting on openness, offering over 100 free tools to anyone willing to download and register. The entry point is simple: log in via the left-hand “Agents” page, no complex configuration required.

The distinction here is stark. While competitors debut “Super Agents” with limited, fixed toolsets within their own silos, Nano AI’s approach mirrors real-world collaboration by allowing large models to call upon diverse MCP tools in parallel. This isn’t just about capability; it’s about architecture. By supporting multiple tools running simultaneously, the system simulates how humans tackle complex, generalized tasks rather than following a rigid script.

What stood out to me was the sheer volume of available resources. The official website currently lists over 100 MCP tools, fostering an ecosystem where agents can be built on top of this open infrastructure. This openness is the key word for Nano AI’s Universal Toolbox today, even if the vision of a “universal application” remains a future goal. Users can now construct agents for various scenarios by freely combining these tools.

The platform already displays a mix of official and third-party agents. Crucially, many tools that require payment on other platforms are available for free integration here. This democratization of access changes the economic dynamic for creators who previously had to pay per tool or subscribe to expensive suites.

Free, yes!

To see if this openness translates to utility, I followed the release’s suggestion and tested the “Deep Research Agent.” The prompt was straightforward: The development status of AI glasses products from 2024-2025. There were no buttons for “Web Search” or “Deep Thinking”—just a query box.

The output was clear and the workflow transparent. I watched it move from thinking and planning to execution: searching for information, generating data visualization icons, and writing research reports. The invocation of MCP tools was visible, including sandbox_coder for icons, summary for drafting, and gen_html for creating web versions.

Finally, it delivered three versions: PDF, Word, and a webpage. This level of automated output generation reduces the friction of compiling final assets from raw data.

I think automated report generation saves hours of manual formatting for researchers and analysts. For creators, free access to premium-style tools lowers the barrier for independent creators. On licensing, transparent tool invocation helps users understand how their data is being processed.

With basic capabilities confirmed, I looked at other agents empowered by this open ecosystem. I selected two that particularly impressed me: Xiaohongshu Browsing Bot and Professional Paper Search. Note that both are developed by third-party developers but can be used for free by ordinary users. Let’s start with the Xiaohongshu Browsing Bot. The Beijing International Film Festival concluded yesterday, so let’s see how users evaluated this year’s event.

Search and analyze Xiaohongshu users’ evaluations of the Beijing International Fi

Super Agents for Everyone: Choose from 100+ MCP Tools, with Stunning Xiaohongshu Scraping Results

The democratization of agent workflows shifts power from specialized developers to everyday creators, but it also raises urgent questions about data provenance and the sustainability of scraped content.

lm Festival.

Aside from a step requiring manual login, the entire process was smooth. This allowed us to observe its versatile operations.

It automatically inputs keywords, clicks through items one by one to view Xiaohongshu posts, and extracts key information. The operation mimics human behavior but achieves higher efficiency—how is that possible?!

From 17 Xiaohongshu posts, it extracted the following key insights. Readers interested in the Beijing International Film Festival might find these resonant~

Furthermore, if you locally set up an automatic Xiaohongshu posting tool, a single command can handle everything from finding trending topics and generating viral content (including images and videos) to publishing. Users can manage their personal self-media accounts on Xiaohongshu with zero intervention.

I think automated scraping without attribution risks devaluing original creator labor. For creators, zero-intervention posting tools may trigger platform anti-spam penalties for users.

Next, let’s look at the “Professional Paper Search” agent, which is more relevant to our daily work. This agent can invoke tools such as Nano AI Super Search, arXiv search, Google Scholar, and other academic search engines.

Recently, I needed to communicate with experts in the field of model compression to understand the latest paper developments. So, I posed this question to it:

Help me retrieve the latest hot papers on “model compression,” display the abstract for each, and include links to the papers.

It ultimately provided four papers, complete with titles, abstracts, and direct links.

After verifying each link one by one, I found that all paper links were valid and the papers were published within the last 1–2 months.

This was quite surprising. It means that agents have now overcome the potential hallucination issues inherent in large models, fully achieving a closed loop from understanding to action.

In the past, posing such questions to large models (even with web search enabled) often resulted in invalid links, an inability to understand specific research content, or a failure to adhere to accurate timelines. These problems have now been circumvented.

On licensing, reliable academic retrieval reduces friction for researchers but increases citation pressure on authors. I think seamless tool invocation demands clearer licensing standards for underlying data sources.

Beyond official and third-party options, users can also create their own agents tailored to specific needs to invoke various tools.

Developers can also configure their own MCP tools/services with just a few parameter settings.

Throughout this process, it is evident that ordinary users find it very convenient to use. With just a single prompt, the agent can automatically analyze user needs, break them down into multiple sub-tasks, autonomously invoke MCP tools (such as browsers and code editors) to execute tasks, and output complete result reports.

The scenarios and capabilities it covers are merely the tip of the iceberg:

According to introductions, the current MCP ecosystem already covers office collaboration, academia, life services, search engines, finance, media and entertainment, data scraping, and other scenarios.

As more MCP tool applications join the platform, the value boundaries of large models and agents will expand infinitely.

Super Agents for Everyone: Choose from 100+ MCP Tools, with Stunning Xiaohongshu Scraping Results

Why Build an Open MCP Ecosystem?

Looking back at the Model Context Protocol (MCP), its industry impact goes far beyond a unified standard allowing large models to access various tools. As demonstrated by Nano AI, what MCP delivers is a triple breakthrough in technology, functionality, and application scenarios. This shift fundamentally alters who controls the creative stack: developers gain modular freedom, while users face new questions about data provenance when agents scrape public platforms like Xiaohongshu.

For creators, open toolkits empower creators to build custom workflows without vendor lock-in. On licensing, scraping tools risk violating platform terms and devaluing original content rights. I think local browser processing offers a privacy-first alternative to cloud-based data harvesting.

Expanding Agent Capabilities with Ease

First, expanding the functions of large models and Agents has become significantly easier. Developers no longer need to build various interfaces and establish communication methods with external data sources, which can be laborious. However, through the unified MCP (Model Context Protocol) data standard, large language models and AI agents can directly connect to a vast array of external tools, allowing them to combine functionalities freely like building blocks.

Autonomous Thinking Over Fixed Workflows

Secondly, agents are learning higher-order autonomous thinking; AI is no longer just a robot that follows fixed workflows. Through the MCP protocol, they can proactively acquire information like humans. For instance, they can select necessary functions from an “all-purpose toolbox” (such as checking the weather or writing code), accumulate experience through trial and error, and become smarter with use. Just as interns grow into experts, AI can gradually build its own decision-making systems.

Unlocking Complex Real-World Tasks

Finally, by freely combining large models with a massive number of MCP tools, complex real-world tasks can be accomplished, significantly broadening application scenarios and truly achieving the concept of ‘the Tao produced One; One produced Two; Two produced Three; Three produced All Things.’ Currently, the Nano AI ecosystem boasts over 100 high-quality, ready-to-use MCP tools and skills that are free to call. Not only does it lead competitors in the number of MCP tools, but it is also simple and easy to use, allowing ordinary users to get started quickly.

For professional developers, this represents the largest and most open MCP tool platform in China. It allows for the free combination of MCP skills to build custom Agent+MCP systems, unlocking new possibilities for agent-based products.

Therefore, when looking at the industry impact brought by MCP, it is not merely about simple tool invocation, but rather providing large models and agent applications with greater potential. More specifically, this refers to the entire ecosystem of large model applications. When large models master the ability to use tools and handle complex tasks with ease, their empowerment in the real world will be significantly advanced.

Thus, it is easy to understand why Nano AI chose a product positioning akin to an “all-purpose toolbox”—to truly anchor itself on the open MCP ecosystem and serve the broad C-end (consumer) user base through simple, low-barrier methods. However, achieving this reality is no small feat. This requires examining the technological and ecological accumulation behind 360, Nano AI’s parent company.

The Power of Search and Multimodal Understanding

First is search capability. On one hand, based on the 360 team’s deep historical expertise in search, they have built a hundred-billion-level index library and a billion-level premium content library. On the other hand, by integrating more MCP-compliant search tools—such as Google Scholar, ArXiv, GitHub, and other common utilities—they stack multiple advantages, making their search capabilities increasingly robust.

The effectiveness of the Xiaohongshu browsing assistant demonstrates its strong ability to understand various modalities of page content. This benefits from the deployment of technologies such as SR (Super Resolution), Vision-Language Models (VLM), PDF layout analysis, and OCR models.

Local-First Browser Architecture

Secondly, there is the accumulation of underlying browser capabilities. Unlike cloud-based AI browsers commonly seen elsewhere, they have developed a dedicated browser specifically for large models that runs locally on personal computers.

Why take this approach? Firstly, large models need to invoke browsers frequently; only by comprehensively transforming the cloud, browser, and OS can high-performance, large-scale concurrent invocation be achieved in a cloud-native environment. Secondly, users or enterprises are often reluctant to entrust private data to third-party cloud servers. Furthermore, some enterprise applications operate within intranets where cloud-based agents c

Super Agents for Everyone: Choose from 100+ MCP Tools, with Stunning Xiaohongshu Scraping Results

For creators, local deployment shifts control back to creators who need data sovereignty over cloud dependencies.

When the cloud isn’t enough, local deployment becomes the mainstream choice for those prioritizing privacy and autonomy. This shift is critical for creators managing sensitive assets or working in restricted environments where external API calls are risky or prohibited.

Finally, there is security deployment. The introduction of isolated sandbox MCPs changes the risk calculus significantly. These systems monitor, warn, and restrict local computer operations by MCP clients in real-time. Users can now confidently allow large models to generate commands for local execution without worrying about data loss, information leakage, or high-risk operations caused by hallucinations, errors, or malicious injection attacks.

On licensing, sandboxing reduces the fear of AI-induced workflow disasters, encouraging broader adoption of agent tools.

The momentum behind this technology is undeniable. Currently, Nano AI’s monthly user visits have surpassed 400 million. With comprehensive support for the MCP protocol and increased adoption by more users and developers, a virtuous cycle of product technology is forming. This growth leads to the emergence of even more advanced agents, expanding the toolkit available to every creator in the stack.

I think as agent capabilities scale, creators must adapt their workflows to leverage these new automated possibilities.

Super Agents for Everyone: Choose from 100+ MCP Tools, with Stunning Xiaohongshu Scraping Results

The “Point-and-Shoot” Era of Agents

Who wins when AI stops requiring a PhD to operate? The creators who can finally focus on output rather than infrastructure. For too long, the creative stack has been gatekept by technical complexity, but that era is ending.

It has become consensus among industry observers that as large models mature, their next evolutionary leap lies in tool usage—specifically, the shift toward autonomous agents.

Think of it this way: just as human bodies and brains evolved to master tools for interacting with the world, large models are now gaining the “hands” to act on their thoughts. As model capabilities strengthen, they acquire the ability to think; but to connect with reality and translate instructions into action, they must use tools.

As the bridge between these models and external utilities, the Model Context Protocol (MCP) consensus is gathering global momentum, sparking an unstoppable wave of integration.

Previously, these advancements were largely internal celebrations within developer and technical circles. Now, led by platforms like Nano AI, the barrier to entry has plummeted, extending agent applications directly to the general public.

Agents have entered their “point-and-shoot camera” stage. This is not just hype; it is an inevitable phase in the technology development cycle.

Everyone is talking about super agents now, but when will they truly arrive? Perhaps it starts with China’s first truly open MCP ecosystem aimed at ordinary users, or perhaps from the moment straightforward terms like “all-purpose toolbox” replace technical jargon like “MCP.”

In any case, this trend has begun and continues to grow. As the core market application under large models, Agents have truly reached their inflection point for explosive growth.

For creators, lowering barriers democratizes access but risks flooding markets with unvetted AI content. On licensing, standardized tools reduce workflow friction for technical creators but may overwhelm non-technical users.