Scaling Intelligently: How MCP Transforms Agent Interconnectivity from Grid to Hub

The left side is what most teams ship first: a custom client per agent, hardcoded to a specific auth flow and response shape for each source. Change the CRM's API and three agents break in three different ways. The right side puts an adapter in front of each source. The adapter speaks MCP, every agent speaks MCP, and swapping a source or adding an agent touches nothing else.

Building an MCP server

An MCP server wraps a single data source and exposes it as a small set of typed tools. This one wraps a CRM lookup and exposes a single tool:

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { ListToolsRequestSchema, CallToolRequestSchema } from "@modelcontextprotocol/sdk/types.js";

const server = new Server(
  { name: "crm-server", version: "1.0.0" },
  { capabilities: { tools: {} } },
);

server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: [
    {
      name: "get_customer",
      description: "Fetch a customer record by ID",
      inputSchema: {
        type: "object",
        properties: { customerId: { type: "string" } },
        required: ["customerId"],
      },
    },
  ],
}));

server.setRequestHandler(CallToolRequestSchema, async (request) => {
  if (request.params.name !== "get_customer") {
    throw new Error(`Unknown tool: ${request.params.name}`);
  }
  const record = await crm.lookup(request.params.arguments.customerId);
  return { content: [{ type: "text", text: JSON.stringify(record) }] };
});

await server.connect(new StdioServerTransport());

Nothing in this file mentions which agent will call it, and that absence is deliberate. The support agent, the sales agent, and a debugging script written next month all reach the same tool through the same interface.

Connecting an agent to multiple servers

The agent side stays just as small. A client connects to each server, lists its tools, and merges the results into the toolset it hands to the model.

const servers = ["crm-server.js", "docs-server.js", "sql-server.js"];
const tools = [];

for (const script of servers) {
  const client = new Client({ name: "support-agent", version: "1.0.0" });
  await client.connect(new StdioClientTransport({ command: "node", args: [script] }));
  const { tools: serverTools } = await client.listTools();
  tools.push(...serverTools.map((t) => ({ ...t, client })));
}

Add a fourth server and this loop picks it up without a code change anywhere else in the agent.

Context windows: pay for what you use

MCP solves the wiring problem and creates a new one. Three servers with ten tools each hand the model thirty tool schemas before a single message arrives, and most of those schemas are irrelevant to the task at hand.

Rank tools by relevance to the current task and load only what fits a fixed budget:

def select_tools(all_tools, task_embedding, token_budget):
    ranked = sorted(
        all_tools,
        key=lambda t: cosine_similarity(t.embedding, task_embedding),
        reverse=True,
    )
    selected, used = [], 0
    for tool in ranked:
        cost = estimate_tokens(tool.schema)
        if used + cost > token_budget:
            break
        selected.append(tool)
        used += cost
    return selected

Apply the same discipline to tool output. A SQL server that returns a 400-row table wastes more context than it saves. Page the results, or summarize before returning them. Don't make the model chew through raw output on every call.

Compute cost drops with the same move. Fewer tokens per call means a smaller bill, and the savings compound across every agent hitting the server all day.

The compounding return

Teams still writing bespoke connectors per agent keep paying the N × M tax as they scale. Standardize on one protocol, and the same investment in a single MCP server pays off for every agent that shows up next.