MCP
Model Context Protocol · Anthropic
Problem: AI needs access to external tools, databases, and APIs.
Layer: Tool integration (AI ↔ systems)
How: JSON-RPC server/client. AI calls tools through structured function interfaces.
Install: SDK + server setup required
Scope: Single agent ↔ its tools
A2A
Agent-to-Agent Protocol · Google / Linux Foundation
Problem: Agents from different vendors need to discover and talk to each other.
Layer: Agent discovery & orchestration
How: HTTP + JSON-RPC. Agents publish Agent Cards, exchange tasks and artifacts.
Install: SDK + HTTP endpoint required
Scope: Agent ↔ agent (cross-vendor)
iLang
AI-Native Communication Protocol · iLang Research
Problem: Natural language instructions are ambiguous. AI guesses instead of executing.
Layer: Instruction & communication (human ↔ AI, AI ↔ AI, AI internal)
How: Pure text protocol. 88 verbs, key-value modifiers, chain syntax. Paste and go.
Install: Zero. Text-based. Tested across ChatGPT, Claude, Gemini, DeepSeek, Kimi, Qwen and GLM.
Scope: Universal communication layer
Feature Comparison
| Feature | MCP | A2A | iLang |
|---|---|---|---|
| Purpose | Tool integration | Agent discovery & collaboration | Communication & instruction |
| Install required | Yes (SDK + server) | Yes (SDK + endpoint) | No (pure text) |
| Human-to-AI | ✗ Not designed for | ✗ Not designed for | ✓ Primary use case |
| AI-to-AI | ○ Via tool calls | ✓ Primary use case | ✓ Supported |
| AI internal reasoning | ✗ | ✗ | ✓ Declaration syntax |
| Behavioral identity | ✗ | ○ Agent Cards | ✓ ::GENE{} DNA system |
| Cross-model portable | ○ Server-side | ✓ | ✓ Tested across ChatGPT, Claude, Gemini, DeepSeek, Kimi, Qwen and GLM |
| Formal vocabulary | ✗ Free-form | ✗ Free-form | ✓ 88 verbs, 29 core modifiers plus a 20-key media profile |
| Token efficiency | N/A (infrastructure) | N/A (infrastructure) | 67% on a request as people write it (169 to 55 tokens), 5% on a terse rewrite (58 to 55), texts on ilang.ai/prompt-compression/ |
| Open source | ✓ Apache 2.0 | ✓ Apache 2.0 | ✓ MIT |
| Backed by | Anthropic | Google + Linux Foundation | iLang Research / iLang Inc. |
How They Work Together
The three protocols operate at different layers of the AI stack. A practical deployment might use all three:
┌─────────────────────────────────────────────┐
│ Human writes iLang instruction │ ← iLang
│ [GET:@SRC|path=sales_q3]=>[STAT]=>[Ω] │
├─────────────────────────────────────────────┤
│ Agent A receives, plans execution │ ← iLang (internal)
│ ::GENE{analyst|conf:confirmed} │
├─────────────────────────────────────────────┤
│ Agent A calls database tool via MCP │ ← MCP
│ {"method":"query","params":{"sql":"..."}} │
├─────────────────────────────────────────────┤
│ Agent A delegates visualization to Agent B │ ← A2A
│ Task: generate chart from this data │
├─────────────────────────────────────────────┤
│ Agent B returns chart artifact │ ← A2A
│ Agent A formats final output │ ← iLang
│ [FMT|fmt=md]=>[Ω] │
└─────────────────────────────────────────────┘When to Use What
| You need to... | Use |
|---|---|
| Give AI access to your database, files, or APIs | MCP |
| Let agents from different vendors collaborate | A2A |
| Write precise instructions that AI follows with fewer retries | iLang |
| Define AI personality, behavior, and identity | iLang (declaration syntax) |
| Chain multi-step workflows in a single instruction | iLang |
| Build tool servers for Claude/GPT | MCP |
| Publish agents for cross-platform discovery | A2A |
Frequently asked questions
What is the difference between MCP and A2A?
As ilang.ai/mcp-vs-a2a/ puts it: MCP connects AI to tools; A2A connects agents to agents. MCP, from Anthropic, solves access to external tools, databases and APIs through a JSON-RPC server and client, and needs an SDK plus server setup. A2A, from Google and now the Linux Foundation, solves cross-vendor agent discovery: agents publish Agent Cards and exchange tasks and artifacts over HTTP and JSON-RPC. iLang sits at a third layer, communication and instruction, and is pure text.
Can MCP, A2A and iLang be used together?
Yes. The compare page walks one deployment through all three. A human writes an iLang instruction, [GET:@SRC|path=sales_q3]=>[STAT]=>[Ω]. Agent A plans it internally with ::GENE{analyst|conf:confirmed}. Agent A calls a database tool through MCP. It delegates a chart to Agent B through A2A. Agent B returns the artifact and Agent A formats the final output with [FMT|fmt=md]=>[Ω]. Different layers, one stack.
Try iLang Now
Unlike MCP and A2A, the iLang protocol itself requires no installation. Copy the protocol header, paste it into ChatGPT, Claude, Gemini, DeepSeek, Kimi, Qwen or GLM, and start using structured instructions immediately.