Building Smarter AI Pipelines with the Model Context Protocol

Most AI workflows hit the same ceiling eventually: the model is smart, but it can't see your data, can't query your tools, and can't take action in the systems your business actually runs on.

Traditionally, solving this meant writing bespoke glue code — a custom integration for each tool, different schemas for each API, and a growing pile of one-off connectors to maintain. Every new capability meant new code.

The Model Context Protocol (MCP) is a different approach. It's an open standard that defines a uniform interface for giving AI agents access to external tools and data sources. Instead of one-off integrations, you write one integration per tool — and any MCP-compatible agent can use it.

What MCP is

MCP was introduced by Anthropic in late 2024 as an open protocol for connecting LLMs to the real world. The core idea is simple: rather than building a custom integration for every combination of model and tool, you build one MCP server per tool and one MCP client per model runtime. The protocol handles the rest.

An MCP server exposes a set of tools — discrete, callable functions with a name, description, and typed input schema. A tool might be "search the web", "query a database", "read a file", "send a Slack message", or anything else an agent might need to do.

An MCP client (your agent runtime) connects to one or more MCP servers, discovers the tools each server exposes, and can call those tools on behalf of the LLM when the model decides it needs them.

From the model's perspective, tools arrive as a list of callable functions. The model selects the right tool, generates the input, and the runtime executes the call and feeds the result back into the context.

Why it matters for AI pipelines

Before MCP, adding a new tool to an AI pipeline typically meant:

Writing a custom function or API wrapper
Formatting it into a tool schema the model could understand
Wiring up the execution logic
Repeating this for every model or framework you wanted to support

This worked — but it didn't compose. Switching models meant rewriting tool definitions. Sharing tools across teams meant duplicating code. Keeping integrations up to date as APIs changed fell to whoever built them.

MCP makes tools portable. An MCP server you build for one workflow is immediately usable in any other workflow that speaks MCP. A Slack MCP server, a GitHub MCP server, a database query server — write them once, reuse them everywhere.

For teams building more than one AI pipeline, this compounds quickly.

How MCP servers work in practice

An MCP server is a lightweight process that:

Declares its available tools (name, description, input schema)
Exposes a transport layer (typically stdio or HTTP with SSE)
Executes tool calls and returns structured results

Here's a simplified example of what a tool declaration looks like from the server's perspective:

{
  "name": "search_crm",
  "description": "Search for contacts or accounts in the CRM by name or email",
  "inputSchema": {
    "type": "object",
    "properties": {
      "query": { "type": "string", "description": "Search term" },
      "limit": { "type": "integer", "default": 10 }
    },
    "required": ["query"]
  }
}

The MCP client sends this definition to the model alongside the user's task. If the model decides it needs to search the CRM, it emits a structured tool call. The client intercepts it, routes it to the right server, and returns the result.

The model never talks to your CRM directly. The MCP server is the boundary — it validates inputs, executes safely, and returns structured output. This separation keeps your pipelines auditable and your integrations maintainable.

The MCP ecosystem is growing fast

Because MCP is an open standard, the ecosystem of available servers is expanding rapidly. Today you can find community-maintained MCP servers for:

Tool	What it exposes
GitHub	Repos, issues, PRs, file contents
Slack	Channels, messages, users
Google Drive	Files, folders, search
PostgreSQL / SQLite	Query execution, schema introspection
Brave Search	Web search results
Filesystem	Local file read/write
Notion	Pages, databases, blocks
Jira	Issues, projects, comments

Any of these can be connected to an MCP-compatible agent runtime and made available to your workflows without writing a single line of integration code.

MCP in CipherSense Agents

CipherSense Agents supports MCP natively. You can connect any MCP server to your workspace, and the platform auto-discovers every tool the server exposes. Those tools then become available to any AI node in any workflow — no manual schema definition, no custom execution code.

This means you can:

Plug in community servers for tools like GitHub, Slack, or Notion and use them immediately in agentic workflows
Build internal MCP servers that expose your proprietary APIs, databases, or business logic — and have every workflow in your organisation share those integrations without duplication
Combine tools dynamically — an agent can call a CRM tool, a calendar tool, and a drafting tool in a single workflow without you pre-wiring those calls

The agent decides at runtime which tools to call and in what order, based on the task. You define the tools that are available; the model decides how to use them.

A practical example

Consider a sales workflow: a new inbound lead comes in, and you want an agent to research the company, check your CRM for existing contacts, draft a personalised outreach email, and route it for human review before it's sent.

With MCP, this breaks down cleanly:

Web search MCP server — agent looks up the company and recent news
CRM MCP server — agent checks for existing contacts and past interactions
Email draft tool — agent generates a personalised draft using what it found
Human-in-the-Loop gate — the draft is paused for a rep to review and approve

Each of those tools is maintained independently. Swap your CRM, update your search provider, or add a new tool — the workflow doesn't need to change.

Building your own MCP server

If you have internal APIs or data sources you want to expose to your AI workflows, building a custom MCP server is straightforward. The MCP SDK is available for TypeScript and Python.

A minimal server in TypeScript looks roughly like this:

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

const server = new Server({ name: "my-internal-api", version: "1.0.0" });

server.setRequestHandler("tools/list", async () => ({
  tools: [
    {
      name: "get_order_status",
      description: "Get the current status of an order by ID",
      inputSchema: {
        type: "object",
        properties: { order_id: { type: "string" } },
        required: ["order_id"],
      },
    },
  ],
}));

server.setRequestHandler("tools/call", async (request) => {
  const { name, arguments: args } = request.params;
  if (name === "get_order_status") {
    const status = await fetchOrderFromYourAPI(args.order_id);
    return { content: [{ type: "text", text: JSON.stringify(status) }] };
  }
});

const transport = new StdioServerTransport();
await server.connect(transport);

Deploy this alongside your services, connect it to CipherSense Agents, and every workflow in your organisation can query your order system — with the model deciding when and how to use the tool.

What to think about when adopting MCP

MCP handles the interface contract, but a few things are still your responsibility:

Tool descriptions matter. The model selects tools based on their names and descriptions. A vague description leads to misuse or no use. Write descriptions as if you're explaining the tool to a capable-but-literal junior engineer: what it does, when to use it, and what the inputs mean.

Keep tools focused. A tool that does too many things is harder for a model to use correctly. Narrow tools with clear semantics perform better than broad tools that require the model to interpret intent.

Return structured outputs. Where possible, return JSON rather than prose. Structured outputs are easier for the model to reason over and pass to downstream steps.

Scope your permissions carefully. An MCP server that exposes write operations should only do so when necessary. Prefer read-only tools for research-phase agents, and gate write tools behind Human-in-the-Loop nodes when the stakes are high.

The bigger picture

MCP is part of a broader shift in how AI systems are built. As LLMs become more capable, the bottleneck is less often the model's reasoning ability and more often its access to context: the right data, at the right time, in the right format.

MCP addresses this by making tool access composable and portable. The same integration works across models, frameworks, and teams. The ecosystem compounds: every well-built MCP server is a building block any agent can use.

For teams building AI pipelines in production, this is the infrastructure worth standardising on now — before you accumulate a patchwork of one-off integrations that are expensive to maintain and impossible to share.

CipherSense Agents supports MCP natively. Connect your first MCP server or explore the workflow canvas to see what's possible.