AI Agents might overtake sooner than you think
So Model Context Protocol (MCP) has been trending for quite some time now, though it was released last August, It is gaining some traction today.
A special focus has come since the release of Blender MCP, where Claude can now control Blender software to generate 3D graphics.
https://medium.com/media/e89df126ad063c0112173f99c49c11e5/href
But why MCP is gaining some traction after one year?
The answer is AI agents.
Why MCP Didn’t Gain Traction Initially
Though MCP was released in August 2024. It was just nowhere on the picture till March 2025, that is this month.
AI agents were not the central attraction back then– When MCP was released last year in August, the major focus was on more intelligent LLMs, and no one was looking at AI agents as a concept. But now that we know AI agents will pave the way for 2025, I think MCP gained traction at the right time.
everyone was craving more intelligent LLMs in 2024
Emerging Technology — MCP was in its early stages, with bugs, compatibility issues, and incomplete documentation. Developers faced challenges in understanding and implementing it, leading to slow adoption.
Anthropic was not great at marketing MCP back then
Potential Fragmentation — There were concerns that different organizations might develop their own versions, leading to inconsistency. Proprietary extensions from big companies could have also limited interoperability, making adoption riskier.
Adoption Challenges — Organizations had already invested in other integration solutions, making the switch to MCP less appealing. The learning curve was steep, and a strong ecosystem of tools was still under development, limiting its immediate utility.
MCPs are difficult to integrate and develop
Why MCP Is Gaining Traction Now
but now all of a sudden in March everyone is talking about MCP. What has changed?
Every other big tech is behind AI agents now — 2025 is the year of AI Agents, and you must know that when we talk about AI Agents, the central stage is the external tools the LLMs can access. Tool calling is decent enough to get started, but for real-world development, I think MCP is the real concept that everyone will be adopting soon.
MCP= Easy tool integration for LLMs
Maturation and Stability — Over time, MCP has improved significantly, with better stability, optimized performance, and clearer documentation. These enhancements have made it more accessible to developers.
MCP is now looking more matured and more stable
Standardization and Interoperability — The AI ecosystem is increasingly prioritizing standardized solutions, and MCP provides a universal method for integrating AI models with external tools. Its ability to maintain context across multiple interactions has made it valuable.
MCP helps in avoiding the fragmentation that we talked about
Industry Adoption — Major tech companies have integrated MCP into their platforms, demonstrating its practical benefits. Seeing real-world use cases in enterprise applications has encouraged broader adoption.
all of a sudden GitHub is full of MCP repositories
Community Engagement — The open-source nature of MCP has led to a growing developer community contributing to its expansion. More shared resources and third-party integrations have made it easier for businesses to leverage MCP effectively.
https://medium.com/media/40a1da3232e88afa0c265e09de1454f2/href
Will MCP affect AI agent growth in the market?
110%
Why?
1. AI Agents Will Have Seamless Tool Integration
- Before MCP, AI agents needed custom APIs for each external tool, creating a fragmented ecosystem.
- MCP provides a universal interface, allowing agents to interact with applications, databases, and web services without additional customization.
- This means faster deployments and easier scaling of AI agents across industries.
Impact: AI agents will be able to connect with business tools (like CRMs, ERPs, or design software) effortlessly, making them more practical for real-world use.
2. Persistent Memory and Context Awareness
- Current AI agents struggle with maintaining long-term memory, often forgetting past interactions.
- MCP allows agents to retrieve and maintain context across different sessions by connecting them to structured data sources.
- Agents can now learn from past interactions, adapt to user preferences, and provide a more personalized experience over time.
Impact: Virtual assistants, customer service bots, and automation agents will become more intelligent and human-like.
3. AI Agents Will Be More Autonomous
- MCP enables agents to pull real-time data and make independent decisions without human intervention.
- Instead of just responding to queries, AI agents will proactively analyze, plan, and act based on updated information.
- This will be a game-changer for automated research, financial trading, cybersecurity, and business operations.
Impact: AI agents will move from reactive tools to proactive problem solvers, capable of self-initiating and optimizing workflows.
4. Multi-Agent Collaboration Becomes Smoother
- Right now, multiple AI agents working together often face coordination issues due to incompatible interfaces.
- MCP standardizes communication, allowing agents to exchange information efficiently without manual data conversions.
- AI agents will be able to work together as teams, each handling different tasks while sharing a unified context.
Impact: More advanced multi-agent orchestration will emerge, leading to powerful AI-driven workflows in business, healthcare, and automation.
5. AI Agents Can Now Interact with 3D & Creative Tools
- The release of Blender MCP showcased how AI can control creative software, generating 3D models from text prompts.
- MCP will extend this capability to video editing, game development, graphic design, and animation, allowing AI to be used more in creative fields.
- Instead of just assisting, AI will now be able to execute and refine creative projects autonomously.
Impact: AI-powered content creation will scale massively, impacting gaming, film, architecture, and design industries.
6. Faster AI Deployment in Enterprises
- Businesses today often hesitate to deploy AI agents due to integration challenges with existing infrastructure.
- MCP removes this barrier by offering a plug-and-play standard, making AI adoption smoother for enterprises.
- AI-powered automation will be easier to implement in customer support, finance, marketing, and HR.
Impact: MCP will accelerate AI adoption across industries, making AI agents an essential part of enterprise automation.
7. Democratization of AI Development
- Previously, only big tech companies with massive resources could build truly autonomous AI agents.
- With MCP, even small businesses and individual developers can create AI agents that interact with complex tools.
- Open-source and community-driven initiatives will expand, leading to more innovation in AI applications.
Impact: A new wave of independent AI startups will emerge, driving fresh ideas and competition in the AI space.
Final Thoughts: The Future of AI Agents with MCP
MCP is set to be a game-changer for AI agents, transforming them from isolated chatbots into fully functional, autonomous, and deeply integrated AI workers. As more platforms adopt MCP, we’ll see AI agents becoming:
More independent (fewer human interventions needed)
More efficient (seamless connections to external tools)
More creative (capable of handling complex artistic and technical tasks)
More accessible (easier for businesses and developers to implement)
In short, MCP is the missing piece that will turn AI agents from promising prototypes into fully practical, scalable, and indispensable AI solutions.
Why is Model Context Protocol (MCP) all over the internet? was originally published in Data Science in your pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
