Coder Legion - Recent posts tagged protocol

MCP (Model Context Protocol): The Standard That Wants to Be the USB of Artificial Intelligence

Sun, 28 Jun 2026 10:49:49 +0000

If you've ever tried building an AI agent system in production, you know the pain. During the construction of the agentic radar for Obsolescence, I faced the problem of connecting Gemini 2.5 with my Supabase database and an external API. I had to write custom code to adapt the tool schema (Tool Calling) to the exact format that Google demands.

If tomorrow I decided to migrate that exact same system to Anthropic's Claude or OpenAI's GPT-4o, I would have to rewrite the entire tool integration layer because each vendor uses its own JSON dialect and its own argument validation logic (Function Calling).

It's the same chaos we lived through in the late 90s with mobile phone chargers: every brand had its proprietary connector. Then USB arrived and unified everything. That is exactly the ambition behind MCP (Model Context Protocol): to become the USB of Artificial Intelligence.

What Exactly is the Model Context Protocol?

Initially proposed by Anthropic and rapidly adopted by a coalition of open-source companies, MCP is an open standard for connecting AI models with data sources and tools.

The design premise is elegantly simple, separating the architecture into two independent pieces:

MCP Hosts: Applications or frameworks where the LLM resides (for example, the Claude desktop app, a LangChain script, or your own Python application).
MCP Servers: Lightweight, small programs that expose data or tools to the Host following a strict standard contract (for example, an MCP server that reads your PostgreSQL database, another that reads your GitHub repository).

The magic happens in the middle. The Host (the LLM) tells the MCP Server: "What tools and resources do you have available?". The server responds in a universal format. From there, the LLM can read, write, or execute actions without the developer having had to write a proprietary integration between that specific model and that specific tool.

Comparison: MCP vs Native Tool Calling

On this blog, I have vehemently defended why Tool Calling is infinitely superior to traditional RAG for industrial applications that require precision. MCP does not replace Tool Calling; it standardizes it.

Let's look at the architectural difference:

Feature	Classic Tool Calling	MCP (Model Context Protocol)
Integration	1-to-1 (Specific Model ↔ Specific Tool)	N-to-M (Any Model ↔ Any MCP Server)
Format	Dictated by the LLM vendor (Google, OpenAI)	Standard, agnostic JSON-RPC 2.0
Discovery	Developer injects tools into the prompt	Host discovers tools dynamically
Portability	None. Migrating LLMs requires refactoring.	Total. You write the MCP server once.

Building an MCP Server: A Real Example

To illustrate why this changes the game for operations and backend engineers, let's imagine we want to expose a Supabase table (e.g., critical component inventory) to our LLM.

With a traditional CrewAI or Langchain approach, we would write a custom tool bound to that framework. With MCP, we write a universal Python server using the official SDK:

from mcp.server.fastmcp import FastMCP
import supabase

# Initialize the MCP server
mcp = FastMCP("Supabase_Inventory_Server")
db = supabase.create_client(URL, KEY)

@mcp.tool()
def get_critical_stock(part_number: str) -> str:
    """Fetches the stock level of a specific component."""
    response = db.table("inventory").select("stock").eq("pn", part_number).execute()
   
    if not response.data:
        return "Component not found."
   
    stock = response.data[0]['stock']
    return f"The current stock for {part_number} is {stock} units."

if __name__ == "__main__":
    # The server starts and listens for requests over stdio (JSON-RPC)
    mcp.run()

That code block is all you need. Once running, any MCP-compatible application (including Claude's official interface) can connect to this server, read the function's description (docstring), and decide when to call get_critical_stock with the correct arguments.

Opinion: Will MCP Be the Definitive Standard?

The history of software is littered with "universal standards" that only managed to add one more standard to the list of competing standards. Will MCP survive?

It has two massive advantages in its favor. The first is that it solves a real, acute pain point for corporate developers, who are sick of rewriting integrations every time a new model comes out. The second is the local-first approach. Standard MCP communication uses stdio (standard input/output), which means the MCP server runs locally on your machine or private network. This is a wet dream for industrial cybersecurity because the data never leaves your infrastructure until the LLM explicitly and authorizedly requests it.

However, MCP's success will depend on adoption by the dominant duopoly: Google and OpenAI. If Anthropic manages to create a large enough open-source ecosystem (like Kubernetes once did against proprietary clouds), the other giants will be forced to support it natively.

If you are designing the architecture for an Agentic Project Management Office or any system where you need to connect AI agents with legacy ERPs, PLMs, or document repositories, my recommendation is to bet on isolating your connectors. Today it might be through independent Python functions, and tomorrow, probably, wrapping those same functions in an MCP Server.

Just as USB killed hundreds of proprietary connectors, MCP has the potential to finally democratize LLM access to the "muscle" of enterprise data.

Sources of Interest:

Authentic AI Protocols: A New Frontier in Autonomous Intelligence

Fri, 30 Jan 2026 03:50:16 +0000

As artificial intelligence accelerates toward greater autonomy, one question has begun to dominate the discourse: Can an AI system truly evolve without leaning on human-created scaffolding? In 2026, a new concept—Authentic AI Protocols—emerged to answer precisely this question. Introduced in a series of critical analyses of data‑free self‑evolving systems, the term marks a significant departure from conventional thinking about how AI learns, adapts, and generalizes.

Despite the rapid expansion of AI research, a striking fact stands out: the concept of authentic AI protocols is almost entirely absent from the global literature. Searches for the term yield little beyond Dr. Syed Muntasir Mamun’s own publications and a few scattered informal mentions online. This rarity is not a weakness—it is evidence of genuine novelty. Authentic AI protocols represent a new epistemic category, one that challenges the assumptions underlying today’s AI architectures.

What Makes an AI Protocol “Authentic”?

At its core, an authentic AI protocol is defined as a standardized, verifiable, and reproducible methodology through which an AI system develops generalizable capabilities without human bias, intervention, or curated inputs. This is not merely a refinement of existing machine learning pipelines. It is a philosophical and technical reorientation.

Most AI systems—even those branded as “autonomous”—depend on human-curated data, human-designed reward structures, or human-indexed tools such as search engines. These dependencies introduce subtle but pervasive biases. They also blur the line between genuine self-evolution and sophisticated mimicry.

Authentic AI protocols reject this entanglement. They demand hermetic autonomy: an AI system must evolve without touching human‑influenced datasets, annotations, or infrastructures. In this sense, the concept is not just technical—it is epistemological. It asks whether intelligence can emerge without inheriting the cognitive fingerprints of its creators.

A Framework for Authenticity

To operationalize this idea, Mamun proposes a set of parameters and tests that distinguish authentic protocols from their symbiotic counterparts.

Parameters include:

• Standardization: clearly defined algorithmic steps
• Verifiability: objective, independent assessment
• Reproducibility: consistent outcomes under identical conditions
• Generalizability: performance beyond the training domain
• Absence of human bias
• Zero human intervention or curation

These are not aspirational ideals—they are measurable criteria. The accompanying tests (reproducibility, autonomy, generalization, bias detection, hermeticity, and convergence) provide a rigorous evaluation framework.

This is where the novelty becomes even more pronounced: no existing AI methodology is evaluated through such a hermetic lens. The field has long accepted human involvement as inevitable. Authentic AI protocols challenge that inevitability.

Mathematical Lenses on a New Idea

One of the most compelling aspects of the concept is its multidisciplinary grounding. Authentic AI protocols can be interpreted through:

• Set theory (mapping initial states to capable states while avoiding human-influenced sets)
• Graph theory (finding human‑free paths through state-transition graphs)
• Algebra (closed autonomous operations within a capability monoid)
• Fractal geometry (self-similar, recursive evolution without external distortions)

These analogies do more than illustrate—they formalize authenticity as a structural property of AI evolution.

Why This Concept Matters Now

As nations and institutions race to build sovereign AI capabilities, the stakes of dependency have never been higher. Authentic AI protocols offer a blueprint for:

• Algorithmic autonomy in environments where human input is impossible
• Agency without human-designed reward signals
• Franchise—delegated, verifiable independence in multi-agent systems
• Democratized AI development, especially for resource‑constrained regions

In a world increasingly shaped by AI, the ability to build systems free from inherited biases and infrastructural dependencies is not just a technical advantage—it is a strategic imperative.

A Novel Concept with Transformative Potential

Authentic AI protocols are not yet mainstream. They are not widely cited. They are not part of standard AI curricula. And that is precisely why they matter. They represent a genuinely new conceptual space, one that invites researchers to rethink autonomy from first principles.

As AI continues its march toward self-directed evolution, this concept may well become foundational. For now, it stands as one of the most intriguing—and novel—ideas in the emerging landscape of autonomous intelligence.