Hermes Agent and MCP: Automate Real Workflows

Hermes Agent, OpenClaw, and Claude Cowork are all called AI agents because they do more than answer questions. They can break an objective into multiple steps, call tools, read data, and produce a complete result. However, comparing these three products using only a feature table can easily lead to the wrong choice. Hermes Agent is designed as an agent that can learn how you work. OpenClaw is designed as a personal assistant that is always available through messaging channels, while Claude Cowork is intended for users who want to delegate office work in natural language within an environment managed by Anthropic. Therefore, the important question is not which tool is the most powerful, but how much you want to manage yourself and where you want the agent to appear in your daily workflow. Three products with different designs The differences among these three AI agent tools do not lie only in the model that performs the work. They also come from the framework surrounding the model, which manages tools, memory, access permissions, and the execution loop. This concept is explained in detail in our article What is an agent harness?, which helps explain why three products that are all called AI agents can behave so differently. Hermes Agent prioritizes a learning loop and execution environments The notable point about Hermes is that skills are not merely a list of skills that have already been installed. After completing a task, the agent can extract a useful process, save it, and improve it the next time. Our article What is Hermes Agent? explains this self learning mechanism separately. The accumulated value of this mechanism grows over time when users have recurring tasks such as analyzing projects, monitoring information sources, standardizing reports, or operating a chain of internal tools. Hermes also supports several types of sandboxes, including local execution, Docker, SSH, Singularity, and Modal. A sandbox is an isolated environment in which the agent executes commands and works with files. This flexibility lets users choose among speed, control, and isolation, but it also requires an understanding of infrastructure, access permissions, and secret management. OpenClaw uses the Gateway as its coordination center In OpenClaw, the Gateway is the control layer between the agent, devices, and communication channels. A message can become a request for the agent to read a calendar, process a file, call a service, or respond in the correct conversation. This approach feels natural for people who want to message an assistant from their phone without needing to remember where the server is running. OpenClaw is most suitable when the agent needs to react as soon as work appears, without requiring the user to open a computer or enter a separate application. Instead of waiting for you to start a work session, it remains available in the messaging channels you already use and begins processing as soon as a message arrives or a configured event is triggered. Claude Cowork provides a managed workspace Cowork reduces the amount of infrastructure that users must manage themselves. In the desktop application, users can grant access to a local folder and ask Claude to read, organize, or create files. With remote sessions, work takes place in an isolated environment on Anthropic servers, which suits long tasks that do not require a personal computer to remain active continuously. In return, the level of customization and control over the execution layer is not as broad as in a self hosted project. Cowork is better suited to people who want quick results within the Claude ecosystem and do not want to maintain a server or design a Gateway themselves. How the memory of the three tools works differently Memory in an agent should not be understood simply as storing every conversation. A useful system must know which information is worth retaining, which information matters only in the current session, and when old data should be retrieved. If it stores too little, the agent must ask the same questions repeatedly. If it stores too much, costs will certainly increase and sensitive data can easily be used in the wrong context. Hermes stands out by combining persistent memory with skills that can improve. Memory records preferences and context, while a skill records how to complete a type of task. These two layers make the agent feel as if it increasingly understands the user, but quality still depends on whether the user reviews what has been stored and removes processes that are no longer appropriate. OpenClaw runs across several channels at once, and that is also its most complicated aspect. Remembering conversation content is only one part of the problem. The harder issue is distinguishing who is speaking, which channel they are using, and which scope the work belongs to. A command sent in a company Slack group should not automatically pull in private context previously discussed on Telegram. If session configuration and identity policies should be established clearly from the beginning, even a strong model cannot rescue a system when everything remains ambiguous. Cowork limits context to each work session, reads only the files for which you grant access, and uses only the connections you allow. For people who are not accustomed to building systems, this approach is easier to control because the boundaries of each task are relatively clear. However, clear boundaries do not mean automatic understanding. You still need to explain what you want, what completion should look like, and where the data should come from. Cowork cannot infer your company context unless you actively provide it. Which type of work each tool automates best Hermes includes web tools, terminal access, MCP, scheduled runs, and subagents. MCP is a connection standard that helps an agent communicate with external data sources or applications through a consistent interface. By combining MCP with skills, users can turn an experiment into a repeatable process, such as collecting data each morning, analyzing changes, and sending a summary. OpenClaw is strong at workflows that begin with a message or an event. For example, a user can send an invoice to a private channel, after which the agent extracts the information and updates a storage system. Another example is receiving a service alert, gathering additional diagnostic data, and returning a summary directly to the operations group. Its value comes from reducing the gap between the moment a need appears and the moment the agent begins acting. Cowork suits structured office outputs. It can research a topic, synthesize data, create a document, and continue revising it according to feedback. Long running or scheduled tasks help Cowork move beyond short question and answer interactions. Even so, organizations need to inspect each connector and its access permissions before allowing the agent to work with real data stores. When deep integration with private infrastructure is required, Hermes and OpenClaw generally provide more room. When the priority is reducing the time from a request to a finished document, Cowork usually has an advantage. This is the difference between a platform intended for assembly and a product that has already been packaged. How secure are these three AI agents? There is no simple answer to the question of which one is safer because the security risks of each tool come from completely different areas. Hermes Agent: Self hosting does not automatically mean safety. The greatest risk comes from automatically generated skills because, in essence, they are pieces of code that the agent writes and then runs by itself. If they are not reviewed before scheduled execution, a skill with terminal access or permission to send data externally can do things without your knowledge. In addition, API keys and sensitive folders should not appear in prompts or be mounted directly into a sandbox when the skill does not actually need them. OpenClaw: The more channels you connect, the wider the attack surface becomes. The point most easily overlooked is sender authentication. If the Gateway trusts only a display name or a channel that has not been properly secured, a compromised messaging account may be enough for someone to issue commands to your agent. The list of people allowed to send commands and the permissions of each bot need to be reviewed whenever you add a new channel. Claude Cowork: The most concerning risk is prompt injection, which occurs when the agent reads a document or webpage containing hidden instructions intended to redirect it away from your original request. Anthropic provides safeguards and asks for confirmation before sensitive actions, but those measures do not replace your own review of the results or the need to avoid granting broader permissions than the task actually requires. Note: With any agent, do not grant permission to delete files, send external messages, or perform sensitive transactions. Start with read only mode, enable complete logging, and retain human approval for actions that require human judgment. Should you choose Hermes Agent, OpenClaw, or Claude Cowork? Every tool has its own strengths and weaknesses, so selecting the most suitable one depends on the user and the work that needs to be done. Choose Hermes Agent when you want the agent to understand how you work increasingly well Hermes suits developers, researchers, and technical teams that want an agent to learn their own processes and run on flexible infrastructure. It is particularly worth considering when tasks recur often enough for skills to create accumulated value. You need to be prepared to read logs, review skills, and manage execution environments. Best suited when: You want the agent to remember and improve work processes through repeated use. You can manage sandboxes, select models, and control access permissions yourself. Choose OpenClaw when work requires continuous communication through messages OpenClaw is suitable when the assistant needs to be present on Telegram, WhatsApp, Slack, Zalo, or similar channels. It is useful for alerts, rapid collection of requests, and automation that begins with a conversation. In return, you must manage identity, channel permissions, and Gateway stability. Best suited when: Requests usually arrive as messages or automated alerts. You need one coordination point for several different communication channels. Choose Claude Cowork when you need quick results without building a system Cowork suits content creators, analysts, and managers who need complete documents, spreadsheets, and slides without wanting to think about servers or Gateways. In return, you should understand the limits of your plan, where data travels, and which connections are enabled before introducing real work. Best suited when: You want to describe the required outcome in natural language and receive a complete output. You prioritize the convenience of a managed service over full control of the infrastructure.

A useful report usually consumes time in three places: finding sources, organizing evidence, and checking whether conclusions are actually supported. Hermes Agent fits this work because web search, browser automation, files, memory, delegation, and skills can live inside one workflow. Once configured well, a manual process can become a reusable routine for weekly reporting, content audits, and keyword research.The practical method shared here is simple: research first, save evidence, draft last, and run SEO as a separate review stage. This is slower than asking an agent to write immediately, but the output is easier to verify and less likely to blur facts with assumptions.Why Hermes Agent fits reporting and SEOAccording to the official Hermes Agent documentation, the system includes more than 60 tools covering web search, browser automation, terminal, files, memory, cron, and delegation. Hermes also has a learning loop that can create skills from experience and improve them during use. That matters when a report or SEO checklist must run repeatedly.If you are new to the platform, start with our introduction to Hermes Agent and its self learning system. This guide focuses on execution: turning a business question into a sourced report and turning a URL into an actionable SEO review.The rule used in this workflow: Hermes may search, extract, organize, and recommend. A human still approves sources, conclusions, primary search intent, and every change that can affect a live website.Four tool groups to enableWeb and search: discover sources and extract evidence.Browser: inspect rendered pages and interact when extraction is insufficient.Files: save briefs, source tables, drafts, and final reports.Memory and skills: retain brand rules, report formats, and proven procedures.Use hermes tools to inspect and configure toolsets. For web research and file writing, enable only the relevant groups instead of granting broad terminal access. A smaller permission scope reduces accidental actions and produces cleaner logs.Set up Hermes Agent for the first runInstall and choose a modelFollow the official quickstart, then run hermes setup to select a provider, model, and tools. Hermes supports Nous Portal, OpenRouter, OpenAI, and custom endpoints, so the reporting workflow does not depend on one model provider.Use a model with sufficient context for source review, but do not spend flagship model pricing on every stage. Discovery, title normalization, and table formatting can use a cheaper model. Contradiction analysis and final synthesis deserve the stronger option.Create a clear workspaceGive each project its own folder with brief.md, sources.md, notes.md, report.md, and seo-audit.md. This structure tells Hermes which file defines the request, which stores evidence, and which is the final output. It also makes later comparisons easier.In a competitor report, I create one folder per month, list five sites in the brief, and require every conclusion to point to a row in sources.md. If a claim lacks evidence, Hermes must mark it as unverified instead of filling the gap.Use Hermes Agent to create a sourced reportStep 1: Write a brief with a completion testA useful brief answers five questions: who will read the report, which decision it supports, what time range matters, which sources are acceptable, and what sections the output must contain. A broad request such as researching the AI market encourages unnecessary scope.The prompt I use is: “Read brief.md. Find no more than 12 official or reputable sources published within the last 90 days. Save the URL, access date, main evidence, and the passage supporting that evidence in sources.md. Do not write the report until the source table is complete. Create a conflict section when sources disagree.”Step 2: Research before writingHermes provides web search and extraction, while browser automation is better for rendered pages and interactions. The Web Search and Extract documentation recommends browser navigation when summarization is not enough. Search broadly first, then open only important sources in the browser.The best output at this stage is not polished prose. It is a clean source table containing title, publisher, URL, date, evidence, and confidence. Missing dates and authors should remain missing rather than being guessed.Step 3: Synthesize around the business questionAfter sources.md is approved, ask Hermes to write report.md and separate facts, interpretation, and action. Readers can then see which statements are supported and which are analytical judgments.During one content report, Hermes found that traffic declined when several pages lost internal links. Instead of claiming a search penalty, the report preserved two hypotheses: weaker internal architecture and lower search demand. The first action was restoring links and observing the result, not rewriting the entire site.A useful verification prompt: ask Hermes to add a section called “What this report does not prove.” It is often the most valuable part of the document.Use Hermes Agent for practical SEOStart with search intentProvide the URL, audience, and conversion goal, then ask Hermes to identify the primary intent, supporting questions, related entities, and page type visible in search results. A keyword list alone often creates content that repeats phrases without satisfying the reader.A practical prompt is: “Inspect this URL with the browser. Identify one primary intent and three supporting intents. Compare the title, headings, introduction, topic coverage, internal links, and schema with five relevant results. Do not recommend keyword density. Every recommendation must name the location to change and the benefit for the reader.”Run an on page checklistHermes can review titles, descriptions, canonicals, hreflang, headings, alt text, internal links, structured data, and duplicate content. Browser automation is useful because it sees the rendered page. Keep audit permissions separate from editing permissions so a review cannot silently modify production.Does the title describe the intent without duplication?Does the introduction answer the main question early?Do H2 and H3 headings create a clear reading path?Do internal links lead to deeper, relevant explanations?Does schema match content visible to users?Are image URLs valid and alt descriptions useful?Create a content brief before draftingDo not ask Hermes to write the SEO article immediately after research. Require a content brief with persona, intent, angle, H2 and H3 structure, evidence, internal links, and unresolved questions. Approve the brief before drafting.For this Hermes Agent guide, the brief includes more than the phrase “Hermes Agent SEO.” It requires an explanation of toolsets, real prompts, browser permission warnings, a link to the foundational article, and a checklist for measuring the result.Turn the workflow into a reusable skillHermes is distinguished by its skill system. The quickstart describes skills as instruction documents loaded when a task matches, while the learning loop can suggest preserving procedures after complex work. Once a reporting workflow succeeds two or three times, convert it into a skill instead of maintaining a long prompt.What a reporting skill should containTrigger conditions and accepted inputs.Required folder and file names.Source selection rules and conflict handling.Report format, source table, and unverified section.A review checklist before delivery or publication.For an SEO skill, add rules that prevent production edits, forbid unsupported schema, and protect canonical URLs. These safeguards are more important than a keyword list because they stop plausible but harmful changes.Schedule reports with cronHermes supports scheduled automation and delivery to messaging platforms. A weekly report can run on Monday morning, collect new evidence, compare it with the previous week, and send a summary. Automate reading and synthesis, but keep publication and SEO changes behind approval.Protect sensitive data: sanitize customer information, limit file and browser access, and use command approval or isolation when Hermes can access a terminal.Mistakes I encounteredThe objective is too broad“Analyze the entire website” produces a long output with little direction. “Find three evidence backed causes for an impressions decline in tool articles” produces a smaller report that supports action. Scope should connect to a decision.Writing begins before source reviewPolished prose can hide weak sources. Separating sources.md from report.md makes errors visible. Review the evidence table before synthesis.SEO is reduced to one scoreA single score does not explain what to change. Hermes should return an issue, evidence, priority, effort, and a validation method. Recommendations that cannot be measured should rank lower.The workflow never becomes a skillIf the same prompt needs correction every week, the learning loop is being wasted. Record mistakes, update the checklist, and improve the skill after every run.How should you start?Choose one small report, no more than five sources, and one clear decision. Create a brief, ask Hermes for a source table, approve the evidence, and only then permit report writing. Use the same evidence for a content brief or SEO audit, but do not let the agent publish automatically.Once the process is stable, save it as a skill and add a final review checklist. Hermes Agent is most valuable not because it writes one report faster, but because it turns a good method into a procedure that can be repeated, inspected, and improved over time.

Learning more makes you better, a principle long assumed to apply only to humans, turns out to hold true for Hermes Agent too, an open-source AI agent from Nous Research. Every time you work with it, Hermes Agent doesn't forget, it remembers, understands you more deeply, and gets better with each session, thanks to a memory system that can recall everything about you even after the machine has been off for a week. What Is Hermes Agent? Hermes Agent is an open-source AI agent developed and released under the MIT license by Nous Research, the lab behind the Hermes, Nomos, and Psyche model lines. Unlike Antigravity or Codex, which depend on an IDE environment, or ordinary chatbots that ultimately remain a thin wrapper calling a single API, Hermes Agent is built to run continuously on a user's own infrastructure, from a cheap VPS to a GPU cluster or serverless infrastructure, and it operates in a way fairly similar to OpenClaw. The core difference in Hermes Agent lies in how it manages long-term memory and converts experience into real skills. Instead of merely storing raw information or passively remembering preferences the way AI like Gemini or Claude do, Hermes runs a closed "learning loop," meaning that after every work session, it actively distills the process into new tools it can use the next time. This system is run by a background "Curator" agent that automatically scores, prunes, and merges accumulated knowledge, combined with FTS5 search technology that retrieves old memories roughly 4,500 times faster without spending any tokens. As a result, Hermes doesn't just respond and forget, it genuinely becomes a collaborator that grows more knowledgeable and capable over time. Four Features That Set Hermes Agent Apart Nous Research doesn't call Hermes Agent a chatbot or a copilot, it positions it as an agent with a built-in learning loop. The four feature groups below explain why that label isn't just marketing. Memory That Persists Across Sessions The biggest weakness of most AI today is that memory only stores raw chat text rather than how work actually gets done. Hermes Agent addresses this through three combined mechanisms: Fast retrieval: Uses FTS5 full-text search to pull up old memories roughly 4,500 times faster than conventional search, without spending extra tokens the way Gemini or Cowork do. User understanding: Integrates Honcho's dialectical user-modeling approach, helping the agent understand preferences, habits, and personal context in depth across thousands of sessions. Continuity: The agent picks up work exactly where you left off, even if that was a project from weeks earlier. Self-Generating and Self-Improving Skills This is the feature that makes Hermes Agent behave like a collaborator that accumulates experience, rather than just a tool that answers on request: Learning from real use: After completing complex tasks, Hermes Agent distills the process into new skills and stores them in a library to be reused automatically next time. Open agentskills.io standard: These skills follow an open standard, so they can be packaged, shared, and reused across different AI systems without being rewritten from scratch. The Curator mechanism: A background administrative agent periodically scores, prunes, and merges duplicate skills, which keeps the skill library from bloating and becoming disorganized over time. Present on More Than 23 Messaging Platforms Hermes Agent isn't confined to a computer, it integrates directly into the messaging channels people already use on their phones every day: Multiple channels, one brain: You can command Hermes Agent through Telegram, Discord, Slack, WhatsApp, Signal, email, or SMS. Context retained: Whether you message via Telegram in the morning or switch to Discord at night, the agent keeps a single thread of memory, never fragmented by channel. Multimodal interaction: Supports sending voice messages, images, and video, along with the ability to analyze multimodal content. Flexible Runtime Infrastructure Hermes Agent supports six backend types for executing commands: local machine, Docker, SSH, Daytona, Singularity, and Modal. With Daytona and Modal, the environment can hibernate when idle and cost almost nothing while waiting, waking up only when there's work to process. This is why Nous Research describes Hermes Agent as an always-on agent that doesn't require users to keep a server running 24/7 at high cost year-round. Hermes Agent can be installed with a single curl command, supporting Linux, macOS, and Windows via WSL2, or, as of June 5, 2026 with version v0.16.0 "The Surface Release," through an official Native Desktop app for Windows, macOS, and Linux with a fully polished GUI, making it accessible to everyday users without needing a terminal. Built-In Toolset and Limitations to Know 40-Plus Built-In Tools, From Web Search to Schedule Automation Hermes Agent ships with more than 40 built-in tools, including web search, browser actions, file handling, and Python script execution via RPC to run sub-tasks without consuming the main agent's context window. A natural-language scheduling system lets you set recurring tasks like daily reports or data backups, then leaves the agent to run them without being reminded. For tasks that need full isolation, Hermes Agent also supports sub-agents with their own conversation, terminal, and scripts, allowing multiple jobs to run in parallel without diluting the main memory. Challenges and Security Considerations Despite rapid updates, Hermes Agent still has a few points users should keep in mind before deploying it: Stability of the self-learning mechanism: The ability to self-improve skills boosts success rates, with a Tencent Cloud report recording gains of up to 52% along with token savings of up to 61%. However, since this is a self-evolving mechanism, real-world effectiveness still depends on the underlying model chosen and still requires human oversight rather than full trust. Risk from high-level permissions, with security responsibility falling on the user: Hermes Agent can intervene deeply in a system (excessive agency), so connecting it directly to multiple messaging platforms requires users to manage their own API keys and set up guardrails. Unlike closed AI services, Hermes Agent hands full control over to the user, which means the user also bears greater responsibility for configuring access permissions to avoid information leaks. Why Is Hermes Agent Growing So Fast? Hermes Agent's growth could be attributed to Nous Research's marketing, but in our view it comes down to three main factors. A Frictionless Migration Path From OpenClaw Recognizing OpenClaw's large user base, Nous Research built a migration tool that lets users carry over their persona, API keys, the entire skill set, and memory to Hermes Agent with a single command, without losing old data and, of course, without having to reconfigure anything from scratch. If you're currently using OpenClaw and want to try Hermes Agent without losing your old data, look for the hermes claw migrate migration tool built into Hermes Agent before considering a fresh install. Betting on a Closed Learning Loop Instead of a Feature Race While many other agents compete on the number of tools they offer, Hermes Agent positions itself as a self-evolving entity, one that distills experience into new skills and retains long-term memory to understand users more deeply over time. This approach creates lasting value, and the community has already put it to use for projects such as automating large-scale content production with high consistency across many sessions. A Role as a Training Data Engine Beyond serving as a personal assistant, Hermes Agent also functions as a capable research tool. It can generate thousands of parallel tool-calling trajectories and compress them into training data for other AI models. By turning the agent's real-world experience into training data, Hermes becomes a platform that developers building the next generation of autonomous AI can't easily do without. How Is Hermes Agent Different From an Agent Harness? People new to the space often confuse Hermes Agent with the concept of an agent harness, which is the framework that decides how a model calls tools, handles the reasoning loop, and coordinates execution steps internally. If a harness is the engine and chassis that determine how a car drives, then Hermes Agent is like a car that already has that engine installed, plus seats, a navigation system, and the driver's own trip memory. In other words, a harness is the technical architecture layer underneath, while Hermes Agent is a complete end-user product that already packages memory, a skill system, communication channels, and a choice of runtime infrastructure. A developer can build their own harness to control every small detail, but most users don't need to go that deep, they just need an agent that runs right away and gets smarter through use. For a closer look at this underlying architecture layer, read more at What Is Agent Harness? The Framework That Makes AI Work Efficiently, which explains in detail how this type of framework operates. Is Hermes Agent Worth Trying Right Now? Being fully open source, collecting no user data, and supporting complete self-hosting, Hermes Agent is one of the few agents today that lets users keep full control over their own data while still getting a continuous assistant experience with real memory, not the simulated memory that only exists within a single chat. After v0.16.0, the biggest technical barrier for users unfamiliar with terminals has largely been removed, as the native desktop app for Windows, macOS, and Linux has fully replaced the pure CLI approach used before. What's left to judge about Hermes Agent isn't whether it runs, but what it learns after a few real weeks of use. The fastest way to find out is to install the desktop app or run the CLI on a cheap VPS, connect it to a familiar messaging channel like Telegram, then watch what skills the agent forms on its own from how you use it every day. That's also the groundwork for comparing Hermes Agent with other options on the market, from Agent Harness to OpenClaw and Claude Cowork, in the next part of this series.

In March 2026, the Model Context Protocol (MCP) SDK reached 97 million monthly downloads, a 970-fold increase just 18 months after its launch. OpenAI, Google DeepMind, Microsoft, and AWS have all integrated MCP into their products. By December 2025, Anthropic officially handed over MCP to the Linux Foundation to make it an open industry standard. MCP is no longer just Anthropic's internal project, but the foundational infrastructure upon which nearly the entire AI industry is building. What is MCP? Model Context Protocol (MCP) is an open protocol released by Anthropic in November 2024. It enables AI models to connect with external data and tools such as Gmail, GitHub, Notion, and internal databases using a single unified standard, eliminating the need for custom integrations per tool. The easiest way to visualize MCP is by comparing it to USB-C. Before USB-C, every device required its own charger cable, creating a clutter of different cables. USB-C emerged to give every device a single standardized port that works everywhere. MCP does the same for AI, providing seamless connectivity to any tool, platform, or data source supporting the standard without custom code for every model-tool combination. How Does MCP Work? Host-Client-Server Architecture MCP is not just a simple two-tier client-server system as commonly thought, but actually defines three distinct roles: Host: The AI application you interact with directly, such as Claude Desktop, Claude Code, or an AI-enabled IDE. Acting as the central orchestrator, the Host manages access permissions and security policies for the entire session. Client: Created by the Host, each Client connects to exactly one Server and handles bidirectional communication between Host and Server. Server: A server connecting directly to the native tool or platform (such as Google Drive, Slack, Email, Calendar, or Database). The Server exposes capabilities and executes actions for the AI. When you connect 3 MCP servers in Claude Desktop, the Host is actually managing 3 distinct Clients, each speaking to its designated Server. Three Core Primitives: Tools, Resources, Prompts Tools: Executable functions that the AI can call to perform actions, such as send_email, create_issue, or search_database. Resources: Data that the AI can read to supply context to the LLM, such as files, records, Notion pages, or database entries. Prompts: Pre-built command templates supplied by the Server to guide the AI on how to use tools effectively for specific tasks or enable quick user triggers. A Concrete Example Suppose you ask Claude "Which recent email mentions contract ABC?". Claude Desktop (Host) initializes a Client connecting to the Gmail MCP Server. This Server calls the Gmail API to search for relevant emails and returns the result in standard MCP format. Claude reads the response and answers you in natural language. For multi-step workflows, like summarizing a YouTube video and saving the summary to Google Drive, Claude calls two different MCP Servers sequentially within the same task, requiring zero manual context switching from you. MCP does not create intelligence on its own; it is simply a standardized connectivity layer. Response quality still depends on the underlying AI model and how well the MCP server implements its tools. How Does MCP Differ from Traditional APIs or Plugins? Before MCP, if you wanted 5 different AI models (Claude, GPT, Gemini, Llama, Mistral) to connect to 5 services (Gmail, Slack, GitHub, Notion, Jira), you theoretically needed to write 25 separate integration pairs — an N×M problem. MCP solves this problem by standardizing the protocol in the middle. We only need to write one MCP server, and every MCP-compliant AI model can use it immediately. The required integrations drop from N×M down to N+M. Traditional Plugins: Each AI platform maintains its own plugin system (e.g., GPT Actions, custom Claude tool use), which cannot be used cross-platform. Traditional APIs: Developers must read documentation, write custom API calling code, and handle authentication per service, typically limited to fixed request-response patterns. MCP: A universal standard — write once and use across the entire MCP-supported AI ecosystem, featuring continuous bidirectional communication where AI can both pull data (read schedules) and push actions (create events) within a single session. When Are Traditional APIs Still Better Than MCP? MCP's flexibility does not mean it is always the best choice. For systems requiring absolute precision and deterministic behavior — such as banking operations like balance checks or wire transfers — traditional APIs with fixed, strictly controlled workflows remain safer. MCP is best suited when you need AI to autonomously decide which tool to call and in what order based on conversational context, rather than rigid transactions requiring strict risk control. Why Is the Entire AI Industry Racing to Adopt MCP? The adoption rate of MCP is unprecedented for a new tech standard. In March 2025, OpenAI officially supported MCP in its Agents SDK and ChatGPT Desktop, despite Anthropic being a direct competitor. By mid-2025, Google DeepMind integrated MCP into the Gemini API. Microsoft brought MCP support to VS Code Copilot, reaching General Availability in July 2025. The biggest turning point occurred on December 9, 2025, when Anthropic handed MCP over to the Agentic AI Foundation (AAIF) under the Linux Foundation. OpenAI and Block co-founded the foundation, while AWS, Google, Microsoft, Cloudflare, and Bloomberg joined as platinum members. This signaled clearly that MCP was no longer Anthropic's proprietary technology, but shared infrastructure that even competitors wanted to build together. Even hardware companies have joined in by opening MCP endpoints for their devices, including smartwatches and heart rate monitors. By July 2026, MCP released its largest spec update to date (2026-07-28), moving the core protocol to stateless, adding an Extensions framework, and introducing OAuth/OpenID Connect authorization. This eliminated the final hurdles for enterprise production deployments. As of 2026, over 10,000 public MCP servers are running in production, and 28% of Fortune 500 companies have deployed custom internal MCP servers. A notable indicator: OpenAI deprecated its proprietary Assistants API in favor of MCP, setting a hard sunset date for mid-2026. When a direct competitor abandons its own standard for Anthropic's open standard, market validation speaks louder than any statement. Practical Application: How to Use MCP with Claude For Claude.ai or Claude Desktop users, connecting an MCP server requires no coding skills. Navigate to Settings → Extensions to view available MCP servers (Google Drive, Notion, Slack, GitHub, Asana...) or add a custom server via URL. Once connected, Claude automatically knows when to invoke specific tools based on your prompts. Here are a few real-world use cases I use daily for 4AIVN editorial work: Claude + Google Drive MCP: Ask directly "Find last week's Gemini 3.7 article outline" instead of searching Drive manually. Claude + GitHub MCP: Review pull requests and read issues without leaving the chat window. Claude + Notion MCP: Update the content calendar database while brainstorming article ideas. Every connected MCP server is granted read/write permissions to your real data. Before enabling an unfamiliar server, verify its developer and requested permissions, especially for servers outside official listings. MCP Will Undoubtedly Keep Growing The most remarkable aspect of MCP is not the protocol itself, but how rapidly it is becoming an implicit standard when users evaluate AI tools. Just as laptop buyers now ask "Does it have USB-C?", in 1-2 years asking "Does this tool have an MCP server?" will likely become a key evaluation criterion for any SaaS or device. This is no longer just a game for OpenAI, Google, or Anthropic; any enterprise or product without MCP integration, regardless of how good it is, risks falling at a disadvantage as users grow accustomed to asking AI directly instead of opening apps manually. For small and medium enterprises, including those in Vietnam, this represents an opportunity rather than pressure. Writing an MCP server does not demand massive infrastructure like building an AI model — wrapping an existing API according to MCP spec is enough for your product to "speak" with Claude, ChatGPT, or any MCP-compliant AI client. Early movers gain a clear competitive edge while user habits are still forming.