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)
I-Lang
AI-Native Communication Protocol · I-Lang 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 | I-Lang |
|---|---|---|---|
| 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) | 68% on a request as people write it (169 to 54 tokens), 7% on a terse rewrite (58 to 54), texts on ilang.ai/prompt-compression/ |
| Open source | ✓ Apache 2.0 | ✓ Apache 2.0 | ✓ MIT |
| Backed by | Anthropic | Google + Linux Foundation | I-Lang 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 I-Lang instruction │ ← I-Lang
│ [GET:@SRC|path=sales_q3]=>[STAT]=>[Ω] │
├─────────────────────────────────────────────┤
│ Agent A receives, plans execution │ ← I-Lang (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 │ ← I-Lang
│ [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 | I-Lang |
| Define AI personality, behavior, and identity | I-Lang (declaration syntax) |
| Chain multi-step workflows in a single instruction | I-Lang |
| 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. I-Lang sits at a third layer, communication and instruction, and is pure text.
Can MCP, A2A and I-Lang be used together?
Yes. The compare page walks one deployment through all three. A human writes an I-Lang 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 I-Lang Now
Unlike MCP and A2A, the I-Lang 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.
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