Model Context Protocol (MCP) Explained: What It Means for Data Infrastructure

AI agents are getting remarkably good at reasoning, planning, and generating text. They can write code, summarize legal documents, draft marketing copy, and hold nuanced conversations about almost anything. But there is a problem that no amount of model intelligence can solve on its own: agents are fundamentally blind without data.

Think about it. You could have the most brilliant analyst in the world sitting in a windowless room with no phone, no internet, and no documents. They might be able to reason through hypotheticals all day long, but they cannot tell you what your revenue was last quarter, how many support tickets came in this morning, or whether that critical database migration completed successfully. Intelligence without information is just speculation.

This is the bottleneck that has quietly held back the entire AI agent ecosystem. The models themselves are extraordinary. What has been missing is a clean, universal way for those models to reach out and touch the real world—to query databases, call APIs, read files, and interact with the tools that businesses actually run on. That is exactly what Model Context Protocol changes, and the implications for data infrastructure are profound.

What Is Model Context Protocol?

Model Context Protocol, or MCP, is an open standard created by Anthropic and released in late 2024. Its purpose is elegantly simple: give AI models a universal, standardized way to connect to external data sources and tools. No more one-off integrations. No more brittle glue code. Just a clean protocol that any AI model can speak and any data source can implement.

The best analogy is USB-C. Remember the days when every phone manufacturer had a different charging cable? You had micro-USB, Lightning, proprietary barrel connectors, and that weird Nokia pin that nobody could ever find. Every time you bought a new device, you needed a new cable. USB-C ended that chaos by providing a single, universal standard that works with everything.

MCP does the same thing for AI. Before MCP, if you wanted an AI agent to access your PostgreSQL database, you wrote custom integration code. If you wanted it to also query your Snowflake warehouse, that was a different integration. Salesforce data? Another one. Every combination of AI model and data source required its own bespoke connector. It was the charging cable problem all over again, just with APIs instead of hardware.

MCP replaces all of that with a single protocol. A data source implements MCP once, and suddenly every MCP-compatible AI model can access it. An AI model supports MCP once, and it can connect to every MCP server in the ecosystem. The combinatorial explosion of custom integrations collapses into something manageable and maintainable.

The Three Primitives

MCP is built around three core primitives that cover the full range of what an AI agent might need from the outside world:

• Resources are the data primitive. They represent structured information that an agent can read—database records, file contents, API responses, configuration values, log entries. When an agent needs to look something up, it accesses a Resource. Think of Resources as the “nouns” of MCP: they are the things that exist in the world.

• Tools are the action primitive. They represent capabilities that an agent can invoke—running a database query, sending an email, creating a record, triggering a deployment. Tools are the “verbs” of MCP. They let agents actually do things, not just read about them.

• Prompts are the template primitive. They are reusable prompt templates that MCP servers can expose to help agents interact with specific data sources more effectively. A database MCP server might expose a Prompt for generating SQL queries, for example, or a documentation server might expose a Prompt for summarizing technical specifications.

Together, these three primitives create a complete vocabulary for AI-to-world interaction. An agent can discover what Resources are available, what Tools it can use, and what Prompts might help it—all through the same standardized protocol.

How MCP Works: Architecture Overview

MCP follows a straightforward client-server architecture, and understanding the flow is important for seeing where data infrastructure fits into the picture.

At the center of the architecture is the Host. This is the AI application—think Claude Desktop, an IDE with AI capabilities, or a custom agent framework your team has built. The Host is what the human user actually interacts with, and it contains one or more MCP Clients.

Each MCP Client maintains a one-to-one connection with an MCP Server. The Client lives inside the Host application and handles the protocol-level communication—discovering available capabilities, making requests, and routing responses back to the AI model.

MCP Servers are the bridge between the protocol and the real world. Each server wraps a specific data source or tool and exposes it through the MCP standard. There is a Postgres MCP server that lets agents query PostgreSQL databases. There is a GitHub MCP server for interacting with repositories and pull requests. There are MCP servers for Slack, Google Drive, file systems, and dozens of other tools. The ecosystem is growing fast.

Here is how the flow actually works when an agent needs data:

1. The agent identifies a need. During a conversation or task, the AI model determines it needs external information—say, the current inventory count for a specific product.

2. The agent invokes an MCP tool. It calls the appropriate Tool exposed by the relevant MCP server. In this case, it might call a query_database tool on the warehouse MCP server with a SQL query.

3. The MCP Client forwards the request. The Client translates the agent’s request into the MCP protocol format and sends it to the correct MCP Server.

4. The MCP Server queries the data source. The server receives the request, connects to the actual data source (a Snowflake warehouse, a PostgreSQL database, whatever it wraps), and executes the query.

5. The response flows back. The data source returns results to the MCP Server, which formats them according to the protocol and sends them back through the Client to the Host, where the AI model can use them to continue its task.

The entire round trip happens in the background. From the user’s perspective, the agent simply “knows” things. But under the hood, MCP is orchestrating a clean, standardized data access flow that can work with any data source that has implemented the protocol.

Transport and Security

MCP supports multiple transport mechanisms. For local integrations (like an MCP server running on your laptop), it uses standard input/output (stdio). For remote integrations, it uses Server-Sent Events (SSE) over HTTP. The protocol handles capability negotiation, so clients and servers can discover what each other supports before exchanging data.

Security is handled at the MCP Server level. Each server controls authentication and authorization to its underlying data source. The protocol itself is designed to be sandboxed—an MCP server for your PostgreSQL database does not automatically get access to your file system or your email. Each server is a separate, isolated integration point.

Why MCP Changes Everything for Data Teams

Here is the shift that data teams need to internalize: for the past two decades, the primary consumers of data infrastructure have been humans. Data engineers built pipelines that fed dashboards. Analytics teams wrote queries that produced reports. The entire data stack—from ingestion to transformation to visualization—was designed around the assumption that a person would be looking at the output.

MCP upends that assumption. With standardized data access for AI agents, the consumer of your data infrastructure is increasingly going to be software. And software has very different expectations than people do.

A human looking at a dashboard can tolerate a chart that is a few hours behind. They can mentally compensate for stale data. They can notice when something looks off and ask a follow-up question. A human consumer is flexible, forgiving, and capable of contextual judgment.

An AI agent is none of those things. When an agent queries your database through MCP, it takes the response at face value. If the data says there are 50 units in inventory, the agent proceeds as if there are 50 units in inventory. If the real number is 12 because the data has not been synced in six hours, the agent will confidently make decisions based on wrong information. There is no gut check, no intuitive “that does not look right” moment. The agent trusts the data completely.

This changes what “good enough” means for data infrastructure. Latency that was acceptable for human dashboard consumers becomes a liability when agents are the consumers. Data that was “close enough” for a weekly report becomes dangerously misleading when an agent is making real-time operational decisions based on it.

New Requirements for the Agent Era

For data teams, the shift to agent-as-consumer introduces several new requirements:

• Structured, queryable data is non-negotiable. Agents cannot interpret a Looker dashboard or squint at a PNG chart. They need structured data they can query programmatically. This means your warehouse, your databases, and your APIs need to be clean, well-documented, and queryable.

• Schema stability matters more than ever. When a human analyst encounters a renamed column, they figure it out. When an MCP server encounters a renamed column, the query breaks and the agent gets an error. Schema changes need to be managed carefully and propagated reliably.

• Data freshness is now an SLA, not a nice-to-have. If your agent-facing data sources are updated via nightly batch jobs, then for up to 23 hours a day, your agents are working with stale information. Real-time or near-real-time data freshness becomes a hard requirement.

• Monitoring and observability need a new dimension. You need to know not just that your pipelines are running, but that the data they deliver is fresh enough for agent consumption. Data freshness SLAs need to be defined, measured, and alerted on.

These are not theoretical concerns. As MCP adoption accelerates, the teams that have already invested in real-time, well-structured data infrastructure will have a significant head start. Those still relying on batch ETL and manual data prep will find themselves scrambling to catch up.

MCP vs Traditional Integration Approaches

To really appreciate what MCP brings to the table, it helps to compare it with the approaches that came before it. Each has its merits, but MCP solves a specific problem that none of the others quite cracked.

Custom API Integrations

This is the oldest and most common approach: write custom code to connect your AI model to each data source. If you want your agent to access PostgreSQL, you write a PostgreSQL integration. If you also want Snowflake, you write another one. Salesforce? Another.

The problem is what engineers call the N times M problem. If you have N AI models and M data sources, you potentially need N times M custom integrations. Five models times ten data sources equals fifty integrations to build and maintain. Each one is bespoke, each one can break independently, and each one needs its own error handling, authentication logic, and data formatting code.

Custom integrations work fine when the numbers are small. But they do not scale, and maintaining them becomes a full-time job as the ecosystem grows.

Workflow Automation (Zapier, Make, n8n)

Workflow automation platforms took a step forward by offering pre-built connectors and visual workflow builders. Instead of writing custom code, you could drag and drop connections between systems.

These tools are excellent for deterministic, rule-based workflows: “When a new row appears in this Google Sheet, create a Jira ticket.” But they were not designed for the fluid, context-dependent way AI agents interact with data. An agent does not follow a fixed workflow. It reasons about what data it needs, formulates queries on the fly, and decides which tools to use based on the task at hand.

Workflow automation tools are rigid where agents need flexibility. They are great for human-defined processes, but they are a poor fit for agent-driven data access.

Framework-Specific Tool Calling (LangChain, LlamaIndex)

AI frameworks like LangChain and LlamaIndex introduced the concept of “tools” that language models can call. This was a big step forward—it gave models a way to reach beyond their training data and interact with external systems.

But these tool definitions are framework-specific. A tool built for LangChain does not work with LlamaIndex. A tool built for LlamaIndex does not work in a custom agent framework. You end up with fragmentation: the same underlying capability (querying a database, for example) gets implemented differently in every framework, with different interfaces, different error handling, and different capabilities.

This is better than raw custom integrations, but it is still a walled-garden approach. Your tools are locked to your framework.

MCP: The Universal Standard

MCP solves the fragmentation problem by operating at a layer below any specific framework. It is not a framework—it is a protocol. Any framework can implement MCP support, and any data source can implement an MCP server.

This means:

• LangChain agents can connect to MCP servers.
• LlamaIndex agents can connect to the same MCP servers.
• Custom agents can connect to those same servers.
• Claude, GPT, Gemini, and any other model can connect through MCP.

Instead of N times M integrations, you get N plus M. Each model implements the MCP client protocol once. Each data source implements the MCP server protocol once. Everyone connects to everyone. The combinatorial explosion is gone.

MCP is winning adoption for several reasons beyond just the math. It is an open standard, not a proprietary lock-in. It has Anthropic’s backing, which brings credibility and resources. The ecosystem is growing rapidly, with MCP servers already available for PostgreSQL, MySQL, MongoDB, Snowflake, BigQuery, GitHub, Slack, Google Drive, and dozens more. And it is model-agnostic—despite being created by Anthropic, MCP works with any LLM that implements the client protocol.

The Data Freshness Problem Behind MCP

Here is where things get interesting for data infrastructure teams, and where the conversation shifts from “how does MCP work” to “what does MCP demand from your data stack.”

MCP solves the access problem. It gives agents a clean, standardized way to reach your data. But it says nothing about the quality or freshness of that data. MCP is a door. It does not care whether the room behind it is clean or a mess.

Let us make this concrete. Say you have an MCP server that exposes your Snowflake warehouse to AI agents. An operations agent queries it to check current inventory levels before approving a large purchase order. The MCP connection works perfectly—the protocol is clean, the query executes, the response comes back in the right format. Beautiful.

But here is the question nobody asks the protocol: when was that Snowflake data last updated?

If your warehouse is fed by a nightly batch ETL job that ran at 2 AM, and the agent is querying at 3 PM, the inventory data is thirteen hours old. A lot can happen in thirteen hours. Products sell. Returns come in. Shipments arrive. The number the agent gets back might be wildly different from reality.

The agent does not know this. It cannot know this. It asked for inventory, it got a number, and it proceeds accordingly. If it approves a purchase order based on phantom inventory, that is a real business problem—and the root cause is not the AI model, the MCP protocol, or the agent framework. The root cause is stale data.

Why CDC Is the Answer

This is precisely where Change Data Capture becomes essential. CDC continuously monitors your source databases for changes and streams those changes in real-time to your downstream systems. Instead of waiting for a batch job to catch up, CDC ensures that your warehouse, your analytics database, and every other system your MCP servers expose reflects the current state of reality.

The math is straightforward. With batch ETL, your MCP-connected data sources can be hours behind. With CDC, the lag drops to seconds or sub-seconds. That is the difference between an agent making decisions on today’s data and an agent making decisions on yesterday’s data.

CDC does not just improve freshness—it also captures the complete change stream. Every insert, every update, every delete is captured and propagated. This means your MCP-exposed data sources are not just recent; they are complete. No changes slip through the cracks between batch windows.

For teams building agent-facing data infrastructure, CDC is not optional. It is the foundation that makes MCP trustworthy.

Architecture: CDC + MCP for Real-Time Agent Data

Let us put the pieces together and look at how CDC and MCP work in concert to create a reliable data access layer for AI agents. The architecture is clean and modular, with each component doing what it does best.

The Reference Architecture

The high-level flow looks like this:

Production Database ---> CDC Platform (Streamkap) ---> Destination (Warehouse/Vector DB/Cache) ---> MCP Server ---> AI Agent

Each arrow represents a different concern:

1. Production Database to CDC: Streamkap connects to your source databases—PostgreSQL, MySQL, MongoDB, DynamoDB, SQL Server, Oracle—and captures every change from the transaction log. This happens with sub-250ms latency and zero impact on production performance.

2. CDC to Destination: Streamkap streams those changes to your destination of choice. This could be a data warehouse like Snowflake, BigQuery, or Databricks for analytical queries. It could be a vector database for RAG (Retrieval-Augmented Generation) workloads. It could be ClickHouse for real-time analytics, or Apache Iceberg for lakehouse architectures. It could even be Kafka for event-driven downstream systems.

3. Destination to MCP Server: An MCP server wraps the destination, exposing its data through the MCP protocol. The server handles authentication, query formatting, and response structuring.

4. MCP Server to AI Agent: The agent connects through MCP, discovers available Resources and Tools, and queries the data it needs. Because CDC keeps the destination current, the agent gets fresh, accurate results.

Pattern 1: Analytics Agents

Source DB ---> Streamkap (CDC) ---> Snowflake ---> MCP Server ---> Analytics Agent

In this pattern, an analytics agent needs to answer questions about business metrics—revenue trends, customer churn, pipeline velocity, operational KPIs. The source data lives in production databases that are off-limits for direct analytical queries. Streamkap streams CDC changes to Snowflake with sub-second latency, and an MCP server exposes the warehouse to the agent.

The agent can run complex analytical queries through MCP without ever touching production systems. And because Streamkap keeps Snowflake in sync with the source databases continuously, the analytics are always based on current data.

Pattern 2: RAG Agents

Source DB ---> Streamkap (CDC) ---> Vector Store ---> MCP Server ---> RAG Agent

Retrieval-Augmented Generation agents need to search and retrieve relevant documents or records to ground their responses in factual data. The quality of RAG depends entirely on the freshness and completeness of the vector store.

Streamkap streams changes from your source databases to a vector store in real-time. When a document is updated in the source system, the corresponding embedding in the vector store is updated within seconds. An MCP server exposes the vector store to the RAG agent, which can search and retrieve the most current information.

Without CDC, the vector store would only be as fresh as the last batch index job. With CDC, it reflects reality continuously.

Pattern 3: Operational Agents

Source DB ---> Streamkap (CDC) ---> ClickHouse ---> MCP Server ---> Operations Agent

Operational agents make real-time decisions—approving orders, routing support tickets, managing inventory, triggering alerts. These agents need the freshest data possible because their decisions have immediate, tangible consequences.

Streamkap streams CDC data to ClickHouse (or a similar real-time analytics database), and an MCP server exposes it to the operational agent. The sub-second latency of the CDC pipeline means the agent is always working with data that is at most a few seconds old.

Why Streamkap for the CDC Layer

The CDC layer is the critical link in this architecture. If it is unreliable, slow, or hard to maintain, the entire agent data access stack suffers. Streamkap’s platform is built specifically for this role:

• Sub-second latency: Changes propagate from source to destination in under 250 milliseconds, end to end. Agents get data that is seconds old, not hours.

• 50+ CDC-optimized connectors: Support for all major sources and destinations, with connectors purpose-built for CDC rather than adapted from batch tools.

• Automatic schema evolution: When source schemas change—columns added, types modified, tables restructured—Streamkap automatically propagates those changes to the destination. This is critical for MCP server stability, because it means the data structures your MCP servers depend on stay consistent without manual intervention.

• Self-healing pipelines: When transient issues occur (network blips, temporary source unavailability), Streamkap automatically recovers without data loss. For agent-facing data infrastructure, reliability is not negotiable.

• Managed Apache Flink for transformations: If you need to transform, filter, or enrich data in-flight before it reaches the MCP-exposed destination, Streamkap provides managed Flink jobs. This means you can tailor the data specifically for agent consumption without building separate transformation infrastructure.

Building for the AI Agent Ecosystem

If you are a data team watching the MCP ecosystem develop and wondering what you should be doing now, here is practical guidance for getting ahead of the curve.

Make Your Data Discoverable

AI agents (and the humans building them) need to find your data before they can use it. This is where standards like llms.txt come in. The llms.txt file is a machine-readable document that describes what your site or service offers, making it easy for AI agents to discover and understand your capabilities.

Streamkap publishes its own llms.txt at streamkap.com/llms.txt, providing AI agents with structured information about Streamkap’s platform, connectors, and capabilities. If you are building services that agents will consume, consider doing the same.

Beyond llms.txt, think about API documentation that is structured for machine consumption. OpenAPI specs, JSON Schema definitions, and clear, consistent naming conventions all make it easier for MCP servers (and the agents that use them) to work with your data.

Invest in Schema Stability

Here is a failure mode that catches many teams off guard: the MCP server is working perfectly, the CDC pipeline is streaming fresh data, and then someone renames a column in the source database. Suddenly, the MCP server’s queries break because they reference a column that no longer exists.

Schema stability is the often-overlooked backbone of reliable agent data access. Every schema change in a source database has the potential to cascade through your CDC pipeline, your destination, your MCP server, and ultimately to every agent that depends on that data.

This is one reason Streamkap’s automatic schema evolution is so valuable in the MCP context. When a source schema changes, Streamkap automatically applies the corresponding change to the destination schema. The MCP server does not encounter a mismatch because the destination has already been updated. It is not a complete solution—the MCP server’s own query definitions may still need updating—but it eliminates the most common and most disruptive failure point.

Monitor Data Freshness as an SLA

When agents are your data consumers, data freshness is not a metric you check occasionally. It is a service-level agreement. If you promise that your MCP-exposed data sources are no more than 30 seconds behind the source, you need monitoring and alerting to enforce that promise.

Define freshness SLAs for each MCP-exposed data source. Instrument your CDC pipelines to report lag continuously. Set up alerts when freshness degrades beyond acceptable thresholds. Treat a freshness SLA breach the same way you would treat a production outage—because for your agents, it is one.

Think About Access Patterns

Agents query data differently than humans do. A human analyst might run a handful of complex queries per day. An agent might run hundreds or thousands of simpler queries per hour. This has implications for how you size your destination systems, how you index your data, and how you design your MCP server’s Tool definitions.

Think about the queries your agents will actually run. Pre-aggregate where it makes sense. Create materialized views for common agent access patterns. Index the columns that agents will filter on. The goal is to make the data not just fresh and accurate, but fast to query at the access patterns agents will use.

Plan for AI/ML Pipeline Integration

MCP is one piece of the broader AI infrastructure puzzle. Your agents will also need training data, evaluation data, feature stores, and model serving infrastructure. The CDC pipelines that power your MCP endpoints can also feed these other systems, creating a unified data foundation for your entire AI strategy.

A single Streamkap pipeline can stream the same source data to multiple destinations simultaneously—Snowflake for analytics agents, a vector store for RAG agents, a feature store for model training, and Kafka for real-time event processing. This multi-destination capability means you are not duplicating CDC infrastructure for each use case.

The Bigger Picture: Who Wins and Who Loses

MCP is part of a broader tectonic shift in how software systems interact. The rise of AI agents as first-class consumers of data and services is going to reshape the technology landscape in ways that are already becoming visible.

The Threat to Point-to-Point Integration Platforms

Traditional integration platforms like Zapier, Make, and Workato built their businesses on being the glue between SaaS applications. They made it easy for non-technical users to connect App A to App B with pre-built connectors and visual workflows.

MCP threatens this model in a fundamental way. If an AI agent can connect to any MCP-compatible data source or tool through a universal protocol, the value of a proprietary connector marketplace diminishes. Why pay for a pre-built Zapier integration when your agent can connect directly through MCP?

This does not mean workflow automation platforms will disappear overnight. They still serve a valuable role for deterministic, rule-based workflows. But the high-growth segment—the intelligent, adaptive, context-aware automations—is shifting toward agent-driven architectures powered by protocols like MCP.

The Companies That Win

The companies that will be best positioned for the agentic AI era share a few common characteristics:

• Their data is fresh. Real-time CDC pipelines keep their systems in sync, so agents always work with current information.

• Their data is structured. Clean schemas, consistent naming, well-documented data models. Agents can discover and query their data without human hand-holding.

• Their data is accessible. MCP servers, well-designed APIs, and machine-readable documentation make it easy for agents to find and use their data.

• Their infrastructure is resilient. Self-healing pipelines, automatic schema evolution, and robust monitoring mean that agent data access does not break when something changes.

These are not new ideas. They are the same principles that have always separated great data infrastructure from mediocre data infrastructure. What MCP does is raise the stakes. When humans were the data consumers, mediocre infrastructure meant delayed insights and frustrated analysts. When agents are the data consumers, mediocre infrastructure means automated systems making confident decisions on wrong information. The cost of getting it wrong goes up dramatically.

The Companies That Lose

Conversely, organizations that are most at risk are those with:

• Batch-only data pipelines. If your data is only fresh once a day, your agents are working blind for 23 hours out of 24.

• Fragile, manually-maintained schemas. Every schema change becomes a potential incident when agents depend on stable data structures.

• Siloed, undocumented data. If your data is scattered across systems with no clear structure or documentation, agents cannot access it through MCP or any other protocol.

• No observability into data freshness. If you cannot measure how stale your data is, you cannot guarantee agent reliability.

The gap between data-mature and data-immature organizations is going to widen as agent adoption accelerates. MCP does not create this gap, but it amplifies it.

Getting Started

The path from here to a reliable, agent-ready data infrastructure is more straightforward than you might think. You do not need to boil the ocean. Start with the fundamentals and build from there.

Step 1: Get Your Data Flowing in Real-Time

If you are still relying on batch ETL to move data from production databases to your analytical systems, that is the first thing to fix. Streamkap provides a zero-ops CDC platform that streams database changes with sub-second latency to 50+ destinations. You can be up and running in minutes, not weeks.

Start with your most critical data sources—the databases that contain the information your agents will need most. Connect them to your warehouse or analytics database through Streamkap, and you will immediately have a foundation of fresh, reliable data for agent consumption.

Step 2: Stand Up MCP Servers

Once your destination data is fresh and flowing, set up MCP servers to expose it. The open-source ecosystem already has MCP servers for most major databases and warehouses. Configure them to connect to your Streamkap-fed destinations, and your agents will have structured, standardized access to current data.

Step 3: Define Freshness SLAs and Monitor

Decide how fresh your agent-facing data needs to be. For operational agents making real-time decisions, sub-second freshness might be the target. For analytics agents answering strategic questions, a few minutes might be acceptable. Set these SLAs, instrument your pipelines, and alert when they are breached.

Step 4: Iterate and Expand

Start with one use case—one source database, one destination, one MCP server, one agent. Prove the pattern works. Then expand to more sources, more destinations, more agents. Streamkap’s pricing starts at $600 per month for the Starter plan and $1,800 per month for the Scale plan, making it accessible to teams of any size.

Ready to Build?

The agentic AI era is not coming—it is here. The protocols are standardized. The tools are available. The question is whether your data infrastructure is ready to support it.

If you want to ensure your AI agents always have access to fresh, accurate, real-time data, start a free trial of Streamkap and see how CDC-powered data infrastructure makes the difference between agents that guess and agents that know.