# Multi-MCP Agent Architecture
## Vision
An agent that transparently accesses tools from multiple MCP servers as if they were all available locally.
```
┌─────────────────────────────────────────────────────────────────┐
│ Agent (using OpenAI, Claude, etc.) │
│ │
│ "Search YouTube for Rust tutorials and save results to memory" │
└────────────────────────┬────────────────────────────────────────┘
│
│ has
▼
┌─────────────────────────────────────────────────────────────────┐
│ ToolRegistry │
│ │
│ Powered by: MultiMcpClientProtocol │
└────────────────────────┬────────────────────────────────────────┘
│
│ routes through
▼
┌─────────────────────────────────────────────────────────────────┐
│ MultiMcpClientProtocol │
│ │
│ Available MCP Servers: │
│ ├─ "local" → http://localhost:8080 │
│ └─ "youtube" → http://youtube-mcp.example.com:8081 │
│ │
│ Tool Registry: │
│ ├─ memory → local │
│ ├─ bash → local │
│ ├─ youtube_search → youtube │
│ └─ youtube_get_transcript → youtube │
└────────────────────────┬────────────────────────────────────────┘
│
┌───────────────┼───────────────┐
│ │ │
▼ ▼ ▼
┌────────┐ ┌────────┐ ┌──────────┐
│ Local │ │ Local │ │ YouTube │
│ MCP │ │ MCP │ │ MCP │
│Server │ │Server │ │ Server │
│ │ │ │ │ │
│memory │ │ bash │ │youtube_ │
│tool │ │tool │ │search │
│ │ │ │ │tool │
└────────┘ └────────┘ └──────────┘
```
## Implementation Flow
### 1. Create MultiMcpClientProtocol
```rust
let multi_mcp = MultiMcpClientProtocol::new();
```
**State after creation:**
- `servers`: empty HashMap
- `clients`: empty HashMap
- `tool_to_server`: empty HashMap
- `all_tools`: None
---
### 2. Register Local MCP Server
```rust
multi_mcp.add_mcp_server(
"local",
"http://localhost:8080".to_string()
).await?;
```
**What happens:**
1. Store server URL in `servers`
2. Create `McpClientProtocol` for that URL
3. Call `client.list_tools()` → returns `["memory", "bash"]`
4. Build `tool_to_server` mapping:
- `"memory"` → `"local"`
- `"bash"` → `"local"`
5. Cache tools in `all_tools`
**State after:**
```
servers = {
"local": "http://localhost:8080"
}
clients = {
"local": McpClientProtocol(...)
}
tool_to_server = {
"memory": "local",
"bash": "local"
}
all_tools = [
ToolMetadata { name: "memory", ... },
ToolMetadata { name: "bash", ... }
]
```
---
### 3. Register Remote YouTube MCP Server
```rust
multi_mcp.add_mcp_server(
"youtube",
"http://youtube-mcp.example.com:8081".to_string()
).await?;
```
**What happens:**
1. Store server URL
2. Create `McpClientProtocol` for that URL
3. Call `client.list_tools()` → returns `["youtube_search", "youtube_get_transcript"]`
4. Update `tool_to_server`:
- `"youtube_search"` → `"youtube"`
- `"youtube_get_transcript"` → `"youtube"`
5. Append tools to `all_tools`
**State after:**
```
servers = {
"local": "http://localhost:8080",
"youtube": "http://youtube-mcp.example.com:8081"
}
tool_to_server = {
"memory": "local",
"bash": "local",
"youtube_search": "youtube",
"youtube_get_transcript": "youtube"
}
all_tools = [
ToolMetadata { name: "memory", ... },
ToolMetadata { name: "bash", ... },
ToolMetadata { name: "youtube_search", ... },
ToolMetadata { name: "youtube_get_transcript", ... }
]
```
---
### 4. Agent Lists Available Tools
```rust
let tools = multi_mcp.list_tools().await?;
// Returns all 4 tools as if they were one registry
```
**Output:**
```
Available tools:
- memory (Store and retrieve key-value data)
- bash (Execute bash commands)
- youtube_search (Search YouTube videos)
- youtube_get_transcript (Get video transcript)
```
---
### 5. Agent Calls a Tool
**Scenario A: Agent calls `memory` tool**
```rust
multi_mcp.execute("memory", json!({
"command": "P search_results [...results...]"
})).await?
```
**Internal flow:**
1. Look up `"memory"` in `tool_to_server` → returns `"local"`
2. Get client for `"local"` server
3. Call `client.execute("memory", params).await`
4. Local MCP server handles it
5. Return result to agent
**Scenario B: Agent calls `youtube_search` tool**
```rust
multi_mcp.execute("youtube_search", json!({
"query": "Rust programming tutorial"
})).await?
```
**Internal flow:**
1. Look up `"youtube_search"` in `tool_to_server` → returns `"youtube"`
2. Get client for `"youtube"` server
3. Call `client.execute("youtube_search", params).await`
4. Remote YouTube MCP server handles it
5. Return results (videos) to agent
---
## Complete Agent Workflow
```rust
// Step 1: Create multi-MCP protocol
let multi_mcp = MultiMcpClientProtocol::new();
// Step 2: Register servers
multi_mcp.add_mcp_server("local", "http://localhost:8080").await?;
multi_mcp.add_mcp_server("youtube", "http://youtube-mcp:8081").await?;
// Step 3: Create registry
let registry = Arc::new(ToolRegistry::new(Arc::new(multi_mcp)));
// Step 4: Create agent
let mut agent = Agent::new(
"researcher",
"Research Assistant",
openai_client
);
agent.tool_registry = Some(registry);
// Step 5: Agent uses tools transparently
agent.send_message(
"Find the top 3 Rust tutorials on YouTube and save them to memory",
None
).await?;
// Agent will:
// 1. Call youtube_search (routed to YouTube server)
// 2. Call memory PUT (routed to local server)
// 3. Return findings to user
```
---
## Benefits
1. **Transparency**: Agent code doesn't know about multiple servers
2. **Composition**: Add/remove servers dynamically
3. **Fault tolerance**: One server down doesn't crash agent (optional: skip that server)
4. **Scalability**: Add as many MCP servers as needed
5. **Type Safety**: Tool discovery and execution still type-safe
---
## Evolution
### Phase 1: Core (Implement First)
- `MultiMcpClientProtocol` struct
- `add_mcp_server()` method
- `tool_to_server` routing
- Basic error handling
### Phase 2: Improvements
- Server health checks
- Tool name conflict resolution (e.g., if two servers have same tool)
- Caching strategies
- Timeout handling per server
- Metrics (which tools called, latency, errors)
### Phase 3: Advanced
- Server priority (if tool exists on multiple servers, use preferred one)
- Failover (if primary server down, try backup)
- Tool aliasing (expose "search" as alias for "youtube_search")
- Rate limiting per server
- Load balancing
---
## Example Use Cases
### Use Case 1: Local + Cloud Tools
```
Local MCP Server: Cloud MCP Server:
├─ filesystem ├─ openai_api
├─ sqlite_db ├─ stripe_payments
└─ bash └─ slack_notifications
```
Agent can orchestrate across both seamlessly.
### Use Case 2: Multiple Third-Party Services
```
YouTube MCP Server: GitHub MCP Server: Slack MCP Server:
├─ youtube_search ├─ get_repo ├─ send_message
├─ youtube_transcript ├─ list_issues ├─ create_channel
└─ youtube_stats └─ create_pr └─ update_thread
```
Agent monitors GitHub, posts updates to Slack, links to YouTube content.
### Use Case 3: Cascading Tool Execution
```
Agent request: "Find trending Rust content and summarize latest discussions"
1. Call youtube_search (remote) → finds video
2. Call youtube_get_transcript (remote) → gets transcript
3. Call bash (local) → processes text with NLP
4. Call memory (local) → stores summary
5. Call slack_notify (remote) → posts summary to team
```
All through one agent interface!