2026-02-08•8 min read

MCP: The Protocol That Will Define How AI Tools Communicate

Name: shodh-memory
Author: Shodh

mcparchitecturestandards

mcp-protocol-future-ai-tools.md

MCP: The Protocol That Will Define How AI Tools Communicate

In the early 1990s, every website had its own protocol for serving pages. Then HTTP won, and the web exploded.

We're at the same inflection point with AI tools. Model Context Protocol (MCP) is doing for AI agents what HTTP did for web browsers: creating a universal language for tools to talk to models.

The Problem MCP Solves

Today, every AI integration is bespoke. Want to connect your memory system to Claude? Write a custom integration. Want the same for Cursor? Write another one. For ChatGPT? Another. Every combination of tool × model requires custom glue code.

```

Without MCP: With MCP:

Tool A → Custom API → Model 1 Tool A ─┐

Tool A → Custom API → Model 2 Tool B ─┤→ MCP → Any Model

Tool B → Custom API → Model 1 Tool C ─┘

Tool B → Custom API → Model 2

(N × M integrations) (N + M integrations)

```

MCP reduces N×M integrations to N+M. That's not an optimization. That's an ecosystem unlock.

How MCP Actually Works

MCP is a JSON-RPC based protocol with a clean abstraction: servers expose tools, resources, and prompts. Clients (AI models) discover and use them.

```

┌─────────────┐ JSON-RPC/stdio ┌──────────────┐

│ AI Model │ ←──────────────────── │ MCP Server │

│ (Client) │ ──────────────────→ │ (Tool Host) │

└─────────────┘ └──────────────┘

│ │

Discovers tools Exposes tools

Calls tools Returns results

Reads resources Serves resources

```

The beauty is in the simplicity. A tool is just a name, a description, and an input schema. The model decides when to call it based on context.

Why Memory Is MCP's Killer App

MCP supports any tool — file access, web search, database queries. But memory is where MCP becomes transformative.

Without MCP memory, every AI session starts from zero. The model has no idea who you are, what you've done, or what you prefer. With MCP memory, the model has cognitive continuity — it remembers.

shodh-memory exposes 45 MCP tools across these cognitive categories:

| Category | Tools | Purpose |

|----------|-------|---------|

| Memory | remember, recall, forget | Core memory operations |

| Context | proactive_context, context_summary | Automatic context surfacing |

| Knowledge | entity operations, graph traversal | Knowledge graph access |

| Tasks | add_todo, list_todos, complete_todo | GTD workflow |

| Reminders | set_reminder, list_reminders | Prospective memory |

| System | memory_stats, backup, verify_index | Health and maintenance |

When Claude Code starts a session, it calls proactive_context with the current conversation. The memory system returns relevant past experiences, decisions, and learned patterns. The model doesn't just respond — it responds with history.

The Ecosystem Forming

MCP is creating a Cambrian explosion of AI tools. Memory servers, browser automation, database connectors, code analysis — all speaking the same protocol.

The winners will be tools that are most useful to AI models. Not the ones with the best dashboards or the most features — the ones that give models the richest context. Memory is inherently the richest context you can provide.

What's Next

MCP is still young. Streaming support is evolving. Multi-modal capabilities are emerging. But the core abstraction — tools as discoverable, callable services — is solid.

The protocol that wins the AI tool layer will be as fundamental as HTTP. MCP has the right design, the right momentum, and the right backers.

Build on it.