Originally proposed by Anthropic, MCP stands for Model Context Protocol and offers a standardized way for AI agents to share memory, discover tools, and act with context. It's quickly gaining traction among platforms that want agents to work together, not just respond in isolation.
In this guide, we'll break down:
- What is MCP and what problem does it solve?
- How does it change the way AI agents interact?
- Why tools like Zapier, Claude, and GitHub are already adopting it
- Real-world examples and benefits
- Where the protocol is headed next
Let's start with a quick definition.
What is MCP (Model Context Protocol)?
MCP is a protocol that enables AI models to discover tools, fetch data, and perform actions across different systems without custom code for every integration. It standardizes how information and tasks are passed between agents, apps, and databases.
Anthropic introduced the MCP in late 2024, as AI agents became more capable but faced the problem of a lack of memory and shared context across tools. It’s a universal set of rules, much like how HTTP allows browsers and websites to talk to each other.
When we talk about "model context", we're referring to the working memory that an AI agent needs to do its job effectively. It's everything the model knows about your tasks, tools, and prior actions without requiring you to explain it repeatedly.
The "protocol" defines how this memory and collaboration work in practice. It's a technical agreement on how agents exchange context and capabilities.
Now that we know the meaning of MCP, let's examine the specific problems it was designed to solve.
What's the goal of the Model Context Protocol?
The primary goal of the Model Context Protocol (MCP) is to give AI agents the ability to share memory, retain task context, and collaborate across tools without needing custom integrations for every action.
LLMs (large language models) impressed everyone with their ability to generate responses. But they were stateless. It means they forgot everything after each interaction. This made it hard for them to handle multi-step tasks or remember essential details across sessions.
Without a shared context, AI agents could only perform isolated actions.
For example, an agent scheduling a meeting could not tell another agent logging it into the CRM that the meeting existed. Everything had to be manually stitched together with brittle APIs or additional coding.
MCP was created to fix this. It provides a standardized schema where agents can read past tasks, add new information, and understand what tools are available at any moment. This allows agents to collaborate like a real team, rather than operating as disconnected bots.
In short, MCP's goal is better collaboration between AI agents and your existing tools.
Next, we look at how it works and why it's different from older approaches like APIs.
How MCP works
MCP works by giving AI agents a shared memory space and task queue. This allows them to discover tools, access data, and perform actions dynamically without needing a hardcoded API connection for each system.
Imagine there's a central "inbox" where all agents — calendar bots, CRM updaters, email drafters — can leave notes, check status, pick up tasks, and collaborate. They can access this inbox as a persistent memory, even as different tasks and tools get involved.
At the heart of the Model Context Protocol are three core components:
- Schema: The schema defines what information gets stored. For example, a meeting invite, a CRM update, or a sales follow-up note. It's structured enough that different agents can understand it without manual translation.
- History: Agents can look back at previous steps –– who did what, when, and what was accomplished. This means they don't start from scratch every time.
- Tools and affordances: MCP keeps a catalog of available tools, things like "send email," "schedule meeting," or "update Salesforce." An agent can dynamically discover what actions are possible without needing to be pre-programmed for each one.
For platforms like Lindy that already have agents that can access CRMs, email platforms, and scheduling tools, an MCP-style memory could naturally extend multi-step automations without rebuilding separate workflows each time.
Now that you have a simple view of how MCP operates, let's quickly compare it to older approaches like APIs and LangChain to see where it fits.
MCP vs API vs LangChain
MCP, APIs, and LangChain all help AI models interact with external systems, but they approach the problem differently. Here’s how:
- APIs are designed for task triggering. You set up a request to a specific service ("send an email," "create a record") and get a response back. They work well for one-off actions but offer no shared memory between tasks or agents.
- LangChain introduced the idea of chaining LLM calls together with some state management. It helps create more complex workflows than plain prompting, but it doesn't fully solve the memory-sharing and dynamic tool discovery problem across different apps.
- Model Context Protocol (MCP) offers persistent shared context — a dynamic, living memory across different AI agents and tools. Instead of hardcoding what each tool can do, agents discover available actions on the fly, work across systems, and maintain an evolving task history.
Let’s summarize them:
- APIs = Single, isolated triggers
- LangChain = Chained model calls with limited memory
- MCP = Full ecosystem memory, task discovery, and collaboration
This shift from isolated actions to ecosystem collaboration makes the MCP benefits powerful for agent-based systems.
Next, we explore examples of how MCP helps today's AI agents to collaborate more intelligently across sales, support, and automation workflows.
Real-world use cases for MCP
Understanding Model Context Protocol is easier when you see some scenarios of it working. Here are a few examples where it makes AI agents more useful across business workflows:
Sales agent logs data after a calendar agent schedules meeting
Imagine a customer books a meeting through your website chatbot. Typically, one AI agent might schedule the calendar event, but then you'd need a separate flow to push that meeting into your CRM.
With MCP, the calendar agent leaves a record in shared memory. The sales agent picks it up, recognizes that the meeting was scheduled, and automatically logs it into the CRM. The agent also updates the lead status and sends a confirmation email.
AI chatbot → AI email follow-up → CRM entry
A website chatbot collects some initial lead info. An email agent drafts a personalized follow-up without you lifting a finger. A CRM agent then logs that interaction history under the right contact record — all without brittle API handoffs.
This smooth cross-agent workflow mirrors how AI automation examples should ideally work: less duct tape, more dynamic memory.
Multi-agent chains in customer onboarding
Agents make onboarding a breeze. Picture an onboarding sequence for a new customer:
- Signup bot creates the account
- Training bot sends learning resources
- Survey bot follows up two weeks later with feedback forms
Each agent relies on what the last one did, without requiring a human to coordinate steps or worry about losing progress. MCP benefits these chains by providing continuity and task awareness between agents.
Cross-tool collaboration between AI and humans
MCP lets an agent updating a Salesforce deal can ping a human via Slack if something looks off. Shared memory means AI agents don't just passively do tasks — they can escalate, notify, and intelligently hand off to humans.
Some platforms, like Lindy, are already architected to support modular agent collaboration — showing that MCP-style memory structures are possible today, even if they're not formally labeled as MCP yet.
Now that you've seen how MCP changes workflows, let's explore how some tools. For example, Lindy naturally aligns with MCP's principles, even before formal standards fully take hold.
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How tools like Lindy align with MCP
Platforms have naturally been building toward Model Context Protocol principles even before it was formally introduced. Lindy is one of those cases, and it mirrors many behaviors MCP aims to standardize.
First, Lindy agents are task-based by design. Each agent is responsible for a specific goal, like scheduling meetings, updating CRM records, or sending follow-up emails, without needing manual babysitting at every step.
These agents retain memory across actions. A Lindy agent who books a meeting doesn't forget it after completing the task. Another agent can use that information to trigger follow-ups or CRM updates, allowing workflows to stay cohesive even across channels like Slack, email, and CRM tools.
Lindy agents also read and update external tools through a total of 2500+ integrations — Salesforce, Slack, Notion, Google Calendar, and more.
Lindy agents use structured workflows that manage internal state, conditional logic, and tool usage — a lightweight form of what MCP aims to formalize. Lindy agents maintain task context across automation workflows instead of relying purely on model memory.
While Lindy doesn't officially brand itself as an "MCP platform," it wouldn't need to radically change to support MCP-style shared memory formats. It's already operating with many of the same assumptions:
- Modular agents
- Persistent task state
- Dynamic interaction across tools
That puts platforms like Lindy in a strong position to adopt MCP standards when they mature or even help define the "structured agent" ecosystem.
Let's know the benefits MCP offers developers, businesses, and AI ecosystems.
What are the benefits of MCP?
MCP unlocks a new level of collaboration between AI agents and tools. Here's where MCP benefits stand out:
Modularity – plug agents into a unified workflow
With MCP, you can connect different agents and tools like building blocks. Instead of hand-coding every connection, agents can dynamically discover available actions, tap into shared memory, and work together without needing hardwired APIs.
Platforms already embracing modular AI agents are a natural fit for this architecture.
Continuity – maintain state over time and tasks
Today, most AI tools lose their memory between tasks. MCP ensures agents remember past conversations, decisions, and actions — even across different sessions or days. This persistence helps agents work more intelligently and reduces the risk of redundant or conflicting actions.
Tool-to-tool integration – cleaner than hardcoded API chains
Instead of custom integrations for every new app or service, MCP creates a standard way for agents to interact with tools. Adding a new CRM, messaging app, or calendar doesn't require rebuilding workflows from scratch.
Developer productivity – fewer duct-taped scripts
When every agent and tool speaks a common protocol, developers spend less time managing custom code and more time building meaningful automations. It's a massive leap from today's patchwork of one-off API connectors.
Ecosystem alignment – shared formats = faster innovation
The more apps and platforms adopt MCP, the faster innovation compounds. Agents can work across company boundaries, tool ecosystems grow more interoperable, and businesses can orchestrate much larger, smarter automation networks.
Companies that design for modular agents, structured workflows, and dynamic task handling today are already ahead of the curve, even if they're not formally on MCP yet.
While MCP offers major advantages, it's still evolving. Let's examine some of its limitations today.
Are there limitations and challenges of MCP?
As promising as the MCP is, it's important to be realistic about its current status. MCP solves real problems, but it's not a perfect system. Let’s see why:
No universal schema yet
Different companies interpret the specification slightly differently. Anthropic's take isn't the same as Zapier's or GitHub's. Developers might still encounter inconsistencies when agents interact across ecosystems without a fully locked universal schema.
Ecosystem fragmentation
Because different organizations are building around MCP in their own ways, there's a risk of early fragmentation. Some versions prioritize different capabilities, authentication methods, or context models, slowing down broad interoperability.
Potential performance tradeoffs
Maintaining a persistent, shared memory across agents isn't cheap. Constant back-and-forth between MCP clients and servers in complex workflows could introduce slight latency or performance hits, especially in real-time applications.
Will require toolmakers to adopt or adapt
For MCP to truly deliver, more platforms, SaaS vendors, and AI builders must adopt it. That's not a small ask. It requires time, engineering resources, and a willingness to align with open standards — something not every company will prioritize immediately.
Next, we’ll see where MCP is headed and why it's shaping up to be one of the most important developments in AI infrastructure.
What does the future of MCP look like?
MCP’s future looks less like a niche tech standard and more like a foundational piece of how AI agents will operate at scale. Here’s what you can expect:
Open standards, maybe foundation-led
Over time, we'll likely see an independent foundation or working group formalize MCP standards, similar to how bodies like W3C standardized how the web works. A neutral steward could ensure that MCP evolves consistently without being dominated by a single company.
LangChain, Zapier, Anthropic, OpenAI — all circling similar ideas
Even companies that didn't start with MCP in mind are converging on similar concepts –– shared memory, tool discovery, and dynamic agent workflows. That alignment suggests that no matter which spec wins out, the ecosystem moves toward an MCP-like future.
Possibility of shared agent registries and permission layers
One of the most exciting possibilities is the creation of shared agent registries — databases where agents can discover tools, services, or other agents securely and dynamically. Combined with permission layers, this would create a safe, scalable framework for cross-tool collaboration.
Platforms like Lindy could help lead by example
Lindy already supports structured workflows, memory retention, and modular automation and is in a position to shape how MCP principles get applied in practice. By demonstrating how AI assistants can work together today, Lindy-like platforms offer a real-world preview of what MCP can make possible at even bigger scales.
While the specifics will evolve, the direction is clear:
- Smarter AI agents
- Persistent memory
- Dynamic, modular ecosystems where tools and agents collaborate naturally
In a few years, building AI systems without shared memory might feel as outdated as manually coding HTML pages without CSS frameworks does today.
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Frequently asked questions
How is MCP different from an API?
Traditional APIs trigger isolated tasks — send an email, create a record, fetch some data.
Conversely, MCP creates persistent shared memory and dynamic task discovery, letting multiple agents work together seamlessly across tools.
Why do AI agents need shared context?
Without shared context, AI agents operate in silos. They forget previous tasks, can't coordinate across systems, and create brittle workflows. Shared context through MCP allows agents to pick up where others left off, maintain continuity, and collaborate intelligently.
What companies are using MCP right now?
Early adopters include Anthropic, GitHub, Zapier, Replit, Hugging Face, and a growing number of open-source contributors.
Can I use MCP with OpenAI or Claude?
Claude (Anthropic) already supports MCP natively through its desktop applications. OpenAI has expressed interest in MCP-style approaches, though formalized support is still evolving.
Is MCP open-source or proprietary?
MCP is fully open-source. The protocol specifications, client/server libraries, and early tooling are all available publicly for anyone to adopt or extend.
Will MCP become an industry standard?
It's moving in that direction. Given the momentum behind open AI agents, dynamic workflows, and multi-agent systems, there's a strong chance MCP becomes the HTTP equivalent for AI agents within the next few years.
Let Lindy be your AI-powered automation app
If you want affordable AI automations with an MCP-friendly platform, go with Lindy. It’s not compatible with Model Context Protocol yet, but it’s an intuitive AI automation platform that lets you build your own AI agents for loads of tasks.
You’ll find plenty of pre-built templates and loads of integrations to choose from.
Here’s why Lindy is an ideal option:
- Automated CRM updates: Instead of just logging a transcript, you can set up Lindy to update CRM fields and fill in missing data in Salesforce and HubSpot — without manual input.
- AI-powered follow-ups: Lindy agents can send follow-up emails, schedule meetings, and keep everyone in the loop by triggering notifications in Slack by letting you build a Slackbot.
- Lead enrichment: Lindy can be configured to use a prospecting API (People Data Labs) to research prospects and to provide sales teams with richer insights before outreach.
- Automated sales outreach: Lindy can run multi-touch email campaigns, follow up on leads, and even draft responses based on engagement signals.
- Cost-effective: Automate up to 400 monthly tasks with Lindy’s free version. The paid version lets you automate up to 5,000 tasks per month, which is a more affordable price per automation compared to many other platforms.
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