Direct MCP requests to the most appropriate models, tools, and services using content-based routing, intelligent load balancing, and dynamic tool routing.
Routing in MCP involves directing requests to the most suitable models or services based on content type, user context, and system load — ensuring efficient processing and optimal resource utilization.
Content-based routing
Route to specialized models based on request type (code, creative, scientific)
Load balancing
Distribute requests across nodes using round-robin, response time, or content-aware strategies
Dynamic tool routing
Route tool calls to regional endpoints, specific API versions, or latency-optimized backends
Content-based routing directs requests to specialized services based on what the request contains. Code generation goes to a code model; creative writing goes to a creative model.
// .NET Example: Content-based routingpublic class ContentBasedRouter{ private readonly Dictionary<string, McpClient> _specializedClients; private readonly RoutingClassifier _classifier; public ContentBasedRouter() { _specializedClients = new Dictionary<string, McpClient> { ["code"] = new McpClient("https://code-specialized-mcp.com"), ["creative"] = new McpClient("https://creative-specialized-mcp.com"), ["scientific"] = new McpClient("https://scientific-specialized-mcp.com"), ["general"] = new McpClient("https://general-mcp.com") }; _classifier = new RoutingClassifier(); } public async Task<McpResponse> RouteAndProcessAsync( string prompt, IDictionary<string, object> parameters = null) { string category = await _classifier.ClassifyPromptAsync(prompt); var client = _specializedClients.ContainsKey(category) ? _specializedClients[category] : _specializedClients["general"]; Console.WriteLine($"Routing to {category} specialized service"); return await client.SendPromptAsync(prompt, parameters); } private class RoutingClassifier { public Task<string> ClassifyPromptAsync(string prompt) { prompt = prompt.ToLowerInvariant(); if (prompt.Contains("code") || prompt.Contains("function") || prompt.Contains("program") || prompt.Contains("algorithm")) return Task.FromResult("code"); if (prompt.Contains("story") || prompt.Contains("creative") || prompt.Contains("imagine") || prompt.Contains("design")) return Task.FromResult("creative"); if (prompt.Contains("science") || prompt.Contains("research") || prompt.Contains("analyze") || prompt.Contains("study")) return Task.FromResult("scientific"); return Task.FromResult("general"); } }}
Tool routing directs tool calls to the most appropriate endpoint based on user context — such as regional endpoints for data residency or versioned endpoints for API compatibility.
# Python: Dynamic tool routingclass McpToolRouter: def __init__(self): self.tool_endpoints = { "weatherTool": "https://weather-service.example.com/api", "calculatorTool": "https://calculator-service.example.com/compute", "databaseTool": "https://database-service.example.com/query", "searchTool": "https://search-service.example.com/search" } self.regional_endpoints = { "us": { "weatherTool": "https://us-west.weather-service.example.com/api", "searchTool": "https://us.search-service.example.com/search" }, "europe": { "weatherTool": "https://eu.weather-service.example.com/api", "searchTool": "https://eu.search-service.example.com/search" } } self.tool_versions = { "weatherTool": { "default": "v2", "v1": "https://weather-service.example.com/api/v1", "v2": "https://weather-service.example.com/api/v2", "beta": "https://weather-service.example.com/api/beta" } } async def route_tool_request(self, tool_name, parameters, user_context=None): endpoint = self._select_endpoint(tool_name, parameters, user_context) if not endpoint: raise ValueError(f"No endpoint available for tool: {tool_name}") return await self._execute_tool_request(endpoint, tool_name, parameters) def _select_endpoint(self, tool_name, parameters, user_context=None): if tool_name not in self.tool_endpoints: return None base_endpoint = self.tool_endpoints[tool_name] # Version routing if tool_name in self.tool_versions: version_info = self.tool_versions[tool_name] requested_version = parameters.get("_version", version_info["default"]) if requested_version in version_info: base_endpoint = version_info[requested_version] # Regional routing if user_context and "region" in user_context: user_region = user_context["region"] if user_region in self.regional_endpoints: regional_tools = self.regional_endpoints[user_region] if tool_name in regional_tools: return regional_tools[tool_name] return base_endpoint async def _execute_tool_request(self, endpoint, tool_name, parameters): async with aiohttp.ClientSession() as session: async with session.post( endpoint, json={"toolName": tool_name, "parameters": parameters}, headers={"Content-Type": "application/json"} ) as response: if response.status == 200: return await response.json() error_text = await response.text() raise Exception(f"Tool execution failed: {error_text}")
The diagram below shows how routing and sampling work together in a comprehensive MCP architecture:
MCP Client │ ▼Request Router ──► Content Analyzer ──► Sampling Configurator │ ▼Load Balancer ──► Server Pool ──► Model Selector │ ┌───────────┼───────────┐ ▼ ▼ ▼ Model A Model B Model C │ │ │ └───────────┴───────────┘ │ Tool Router │ │ Primary Tools Regional Tools
Combine content-aware routing with regional tool routing so that a French user’s weather query is routed to both the creative-writing model (if asking in a conversational tone) and the EU weather endpoint (for data residency compliance).