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The Protocol of Agents: Web3’s MCP Potential

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Starting as an experimental side project at Anthropic, the Model Context Protocol (MCP) has become the de facto standard for orchestrating agentic interactions across datasets, computational resources and external artifacts.

It may represent one of the most transformative protocols for the AI era and a great fit for Web3 architectures.

Much like HTTP revolutionized web communications, MCP provides a universal framework that underpins virtually every major AI platform’s ability to integrate smart agents with diverse information sources and operational endpoints.

A Short Intro to MCP

MCP was initially designed to streamline interactions between prototype agents and document stores. Early success in coordinating retrieval and reasoning workflows caught the attention of other labs, and by mid-2024, researchers had rolled out open-source reference implementations. 

A surge of community-driven extensions soon followed, enabling MCP to support secure credential exchange, federated learning scenarios, and plugin-style resource adapters. By early 2025, leading platforms—including OpenAI, Google DeepMind, and Meta AI—had adopted MCP natively, cementing its role as the HTTP-equivalent protocol for agentic communications.

MCP employs a lightweight client–server paradigm with three principal participants: the MCP Host (an AI application orchestrating requests), one or more MCP Clients (components maintaining dedicated connections), and MCP Servers (services exposing contextual primitives). Each client–server pair communicates over a distinct channel, enabling parallel context sourcing from multiple servers.

MCP’s Data Layer revolves around three foundational primitives—Tools, Resources, and Prompts—that together empower seamless agent collaboration.

Tools encapsulate remote operations or functions that an agent can invoke to execute specialized tasks, while Resources represent the data endpoints—such as databases, vector stores, and on-chain oracles—from which agents can fetch contextual information.

Prompts serve as structured templates guiding an agent’s reasoning process, defining how inputs should be formulated and interpreted. By standardizing these core building blocks, MCP ensures that diverse agents can discover, request, and utilize capabilities in a consistent, interoperable manner across any underlying infrastructure.

MCP and Web3

From a first-principles standpoint, the intersection of Web3 and MCP could materialize in two key areas:

  1. Enabling every blockchain dataset and decentralized protocol to operate as an MCP server or client
  2. Use Web3 to power a new generation of MCP networks.

Together, these imperatives promise an extensible, trust-minimized fabric for agentic intelligence.

Web3 Data as MCP Artifacts

To catalyze AI agents in crypto environments, seamless access to on-chain data and smart-contract functionality is paramount. We envision blockchain nodes exposing block and transaction histories through MCP servers, while DeFi platforms publish composable operations via MCP interfaces.

Complementing this pattern, traditional crypto gateways—exchanges, wallets, explorers—act as MCP clients, uniformly querying and processing context. Imagine a single agent concurrently interfacing with Aave’s lending markets, Layer0’s cross-chain bridges, and MEV analytics, all through the same coherent programming interface.

Web3 MCP Networks

MCP is an incredibly powerful protocol but, just like HTTP, it’s going to evolve from isolated endpoints to powering complete networks. These days, using MCP still requires detailed knowledge of client and server endpoints. Similarly, capabilities such as authentication and identity are core missing blocks from the protocols but essential for the streamline adoption of MCP.

The next phase of MCP is going to be powered by network platforms that enable some more sophisticated capabilities:

  • Dynamic discovery that surface the right MCP endpoints for a given task.
  • Search capabilities that allow agents find the right MCP endpoints.
  • Ratings of MCP servers and clients to tract their reputation.
  • Coordination of MCP servers to achieve a specific outcome.
  • Verifiability of the outputs produced by MCP endpoints.
  • Traceability of the interactions with MCP clients and servers
  • Authentication and access control mechanisms for MCP servers.

Many of these capabilities require the right level of economic incentives to coordinate the nodes in an MCP network. This seems like a match made in AI heaven for Web3. Traceability, trustless and verifiable computations are some of the key primitives that can power the first generation of MCP networks. Web3 is the most efficient technology of several generations to power computation networks and MCP needs new networks.

Project Namda

The idea of combining Web3 and MCP to power a new generation of MCP networks is not theoretical by any stretch and we are starting to see real progress in the space. One of the most interesting initiatives in this area is MIT’s Project Namda.

Spearheaded by researchers at CSAIL and the MIT-IBM Watson AI Lab, Namda was launched in 2024 to pioneer scalable, distributed agentic frameworks built on MCP’s messaging foundations. Namda (Networked Agent Modular Distributed Architecture) creates an open ecosystem where heterogeneous agents—spanning cloud services, edge devices, and specialized accelerators—can seamlessly exchange context and coordinate complex workflows. By leveraging MCP’s standardized JSON-RPC primitives, Namda demonstrates how large-scale, low-latency collaboration can be achieved without sacrificing interoperability or security.

Namda’s architecture already incorporates many of the ideas of a decentralized MCP network such as dynamic node discovery, load balancing, and fault tolerance across distributed clusters. With a decentralized registry inspired by blockchain techniques, Namda ensures verifiable agent identities and policy-driven resource arbitration, enabling trusted multi-party workflows. Extensions for token-based incentive mechanisms and end-to-end provenance tracking further enrich the protocol, with early prototypes illustrating efficient federated learning on vision-and-language tasks across global testbeds.

A Different Foundation for Decentralized AI

For decades, decentralized AI has struggled to find a clear fit to power mainstream AI applications. The emergence of MCP and the need for MCP networks have rapidly become one of the most prominent use cases for a new generation of AI infrastructure. This might be one of the biggest use cases in AI and one that Web3 is perfectly suited to address. The combination of Web3 and MCP might just be a new foundation for decentralized AI.

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XLM Sees Heavy Volatility as Institutional Selling Weighs on Price

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Stellar’s XLM token endured sharp swings over the past 24 hours, tumbling 3% as institutional selling pressure dominated order books. The asset declined from $0.39 to $0.38 between September 14 at 15:00 and September 15 at 14:00, with trading volumes peaking at 101.32 million—nearly triple its 24-hour average. The heaviest liquidation struck during the morning hours of September 15, when XLM collapsed from $0.395 to $0.376 within two hours, establishing $0.395 as firm resistance while tentative support formed near $0.375.

Despite the broader downtrend, intraday action highlighted moments of resilience. From 13:15 to 14:14 on September 15, XLM staged a brief recovery, jumping from $0.378 to a session high of $0.383 before closing the hour at $0.380. Trading volume surged above 10 million units during this window, with 3.45 million changing hands in a single minute as bulls attempted to push past resistance. While sellers capped momentum, the consolidation zone around $0.380–$0.381 now represents a potential support base.

Market dynamics suggest distribution patterns consistent with institutional profit-taking. The persistent supply overhead has reinforced resistance at $0.395, where repeated rally attempts have failed, while the emergence of support near $0.375 reflects opportunistic buying during liquidation waves. For traders, the $0.375–$0.395 band has become the key battleground that will define near-term direction.

XLM/USD (TradingView)

Technical Indicators
  • XLM retreated 3% from $0.39 to $0.38 during the previous 24-hours from 14 September 15:00 to 15 September 14:00.
  • Trading volume peaked at 101.32 million during the 08:00 hour, nearly triple the 24-hour average of 24.47 million.
  • Strong resistance established around $0.395 level during morning selloff.
  • Key support emerged near $0.375 where buying interest materialized.
  • Price range of $0.019 representing 5% volatility between peak and trough.
  • Recovery attempts reached $0.383 by 13:00 before encountering selling pressure.
  • Consolidation pattern formed around $0.380-$0.381 zone suggesting new support level.

Disclaimer: Parts of this article were generated with the assistance from AI tools and reviewed by our editorial team to ensure accuracy and adherence to our standards. For more information, see CoinDesk’s full AI Policy.

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HBAR Tumbles 5% as Institutional Investors Trigger Mass Selloff

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Hedera Hashgraph’s HBAR token endured steep losses over a volatile 24-hour window between September 14 and 15, falling 5% from $0.24 to $0.23. The token’s trading range expanded by $0.01 — a move often linked to outsized institutional activity — as heavy corporate selling overwhelmed support levels. The sharpest move came between 07:00 and 08:00 UTC on September 15, when concentrated liquidation drove prices lower after days of resistance around $0.24.

Institutional trading volumes surged during the session, with more than 126 million tokens changing hands on the morning of September 15 — nearly three times the norm for corporate flows. Market participants attributed the spike to portfolio rebalancing by large stakeholders, with enterprise adoption jitters and mounting regulatory scrutiny providing the backdrop for the selloff.

Recovery efforts briefly emerged during the final hour of trading, when corporate buyers tested the $0.24 level before retreating. Between 13:32 and 13:35 UTC, one accumulation push saw 2.47 million tokens deployed in an effort to establish a price floor. Still, buying momentum ultimately faltered, with HBAR settling back into support at $0.23.

The turbulence underscores the token’s vulnerability to institutional distribution events. Analysts point to the failed breakout above $0.24 as confirmation of fresh resistance, with $0.23 now serving as the critical support zone. The surge in volume suggests major corporate participants are repositioning ahead of regulatory shifts, leaving HBAR’s near-term outlook dependent on whether enterprise buyers can mount sustained defenses above key support.

HBAR/USD (TradingView)

Technical Indicators Summary
  • Corporate resistance levels crystallized at $0.24 where institutional selling pressure consistently overwhelmed enterprise buying interest across multiple trading sessions.
  • Institutional support structures emerged around $0.23 levels where corporate buying programs have systematically absorbed selling pressure from retail and smaller institutional participants.
  • The unprecedented trading volume surge to 126.38 million tokens during the 08:00 morning session reflects enterprise-scale distribution strategies that overwhelmed corporate demand across major trading platforms.
  • Subsequent institutional momentum proved unsustainable as systematic selling pressure resumed between 13:37-13:44, driving corporate participants back toward $0.23 support zones with sustained volumes exceeding 1 million tokens, indicating ongoing institutional distribution.
  • Final trading periods exhibited diminishing corporate activity with zero recorded volume between 13:13-14:14, suggesting institutional participants adopted defensive positioning strategies as HBAR consolidated at $0.23 amid enterprise uncertainty.

Disclaimer: Parts of this article were generated with the assistance from AI tools and reviewed by our editorial team to ensure accuracy and adherence to our standards. For more information, see CoinDesk’s full AI Policy.

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Dogecoin Inches Closer to Wall Street With First Meme Coin ETF

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The first exchange-traded fund (ETF) built around a meme coin could hit the market this week, after multiple delays and much speculation.

The DOGE ETF — formally called the Rex Shares-Osprey Dogecoin ETF (DOJE) — was originally slated to debut last week, alongside a handful of politically themed and crypto-related ETFs. Those included funds tied to Bonk (BONK), XRP, Bitcoin (BTC) and even a Trump-themed fund. But DOJE’s debut never materialized.

Now, Bloomberg ETF analysts Eric Balchunas and James Seyffart believe Wednesday is the most likely launch date, though they caution nothing is certain.

“It’s more likely than not,” Seyffart said. “That seems like the base case.”

Ahead of the introduction of the ETF, DOGE has been among the top performers over the past month, ahead 15% even including a decline of 3.5% over the past 24 horus.

If launched, DOJE would mark a milestone as the first U.S. ETF to focus on a meme coin — cryptocurrencies that generally lack utility or a clear economic purpose. These include tokens like Dogecoin, Shiba Inu (SHIB) and Bonk, which often surge in popularity thanks to internet culture, celebrity endorsements and speculative trading.

Balchunas described DOJE’s significance in a post on X: “First-ever US ETF to hold something that has no utility on purpose.”

DOJE is not a spot ETF. That means it won’t hold DOGE directly. Instead, the fund will use a Cayman Islands-based subsidiary to gain exposure through futures and other derivatives. This approach sidesteps the need for physical custody of the coin while still offering traders a way to bet on its performance within a traditional brokerage account.

The ETF was approved earlier this month under the Investment Company Act of 1940, which is typically used for mutual funds and diversified ETFs. That sets it apart from the wave of bitcoin ETFs that received green lights under the Securities Act of 1933, a framework used for commodity-based and asset-backed products. In short, DOJE is structured more like a mutual fund than a commodity trust.

More direct exposure may be coming soon. Several firms have filed applications to launch spot DOGE ETFs, which would hold the meme coin itself rather than derivatives. These applications are still under review by the U.S. Securities and Exchange Commission (SEC), which has grown more comfortable with crypto ETFs since approving a slate of bitcoin products in early 2024.

The broader crypto market has shown that investor demand can outweigh fundamental critiques. Meme coins have long drawn skepticism for having no underlying value or use case, but that hasn’t kept them from drawing billions in speculative capital.

Seyffart said the ETF market is likely to follow the same path. “There’s going to be a bunch of products like this, whether you love it or need it, they’re going to be coming to market,” he said.

He added that many existing financial products serve no deeper purpose than providing a vehicle for short-term bets. “There’s plenty of products out there that are just being used as gambling or short-term trading,” he said. “So if there’s an audience for this in the crypto world, I wouldn’t be surprised at all if this finds an audience in the ETF and TradFi world.”

Whether the DOJE ETF opens the door to more meme coin funds — or just proves the concept is viable — may depend on how the market responds this week. Either way, it signals a new phase in the merging of internet culture and traditional finance.

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