Explore how MCP enables scalable AI collaboration and agent interoperability.
Large-scale AI often involves many specialized agents working in parallel. While this boosts overall capability, it also challenges traditional single-agent paradigms. Each agent usually has a limited context window, which means important information may be lost between turns. For example, a lead agent might plan a task and delegate it to other agents, but if its plan isn’t stored outside the model’s context, it may be lost. Integrating the various tools and data sources required by each agent is also often error-prone. Developers regularly encounter issues passing data between agents, as incompatible tools frequently lead to execution or parsing problems. Without a single standard way to share context, crucial data stays siloed, making coordination fragile and costly to scale.
The Model Context Protocol standardizes how AI agents communicate with external resources and data. Think of MCP as a universal “port” for AI: a common interface that lets any model connect to calculators, databases, APIs, and other services without custom glue code. In essence, MCP gives every agent in a system a shared way to exchange context and call services. Once MCP is in place, each agent can access contextual information (via Resources) and actionable functions (via Tools) through the same protocol. This resolves earlier challenges: tool integrations follow a shared contract instead of bespoke code, and agents become inherently context-aware and interoperable.
MCP standardizes how AI agents communicate with external resources and data. Think of it as a universal “port” for AI, a shared interface that lets any model connect to calculators, databases, APIs, and other services without requiring custom glue code. In essence, MCP gives every agent in a system a shared way to exchange context and call services. Once MCP is in place, each agent can access contextual information (via Resources) and actionable functions (via Tools) through the same protocol. This resolves earlier challenges: tool integrations follow a shared contract instead of bespoke code, and agents become inherently context-aware and interoperable.
MCP defines two main protocol layers: the Data Layer and the Transport Layer. The Data Layer is based on JSON-RPC and specifies the message formats that agents use. It covers notifications, context and action primitives, and lifecycle events (such as initialization and capability negotiation). These JSON-RPC messages are carried over the Transport Layer, which could be HTTP (with streaming) for remote servers or STDIN/STDOUT for local communication. Because both client and server speak the same JSON-RPC dialect, they can communicate over sockets or pipes without requiring extra integration code.
See how your AI architecture benefits from MCP integration.
MCP makes it easy for agents to use external memory and persistence. Since the protocol is independent of any particular LLM, an MCP server can offer write capabilities or wrap a database/vector store as a resource. In practice, this lets an agent deliberately dump its state outside the short-live discussion. For example, a lead researcher agent can draft a literature review plan and store it in an external memory server via MCP before launching subtasks. Later, even if the agent’s context window has shrunk, it can retrieve the stored plan and continue without losing progress.
After completing tasks, agents can save key information in external memory and summarize finished work phases. Instead of losing progress, they simply start a new agent instance and reload the context from memory when needed. This approach prevents context overflow and maintains continuity in long, complex workflows. In MCP terminology, a “Memory” is just another server: it might offer a readMemory resource and a writeMemory tool. Any connected agent can write to or read from this memory server as needed.
Shared persistence also aids collaboration among agents. Rather than passing large chunks of text directly, agents write intermediate results to a common store. One useful pattern is the “artifact” technique: a sub-agent might save its structured result (for example, a JSON report or file) to a file-system MCP server and return a small reference (or file path) to the lead agent. This greatly reduces token usage and avoids long chains of data being passed between agents. Later, any agent can retrieve these results by calling the resource on the memory server. In MCP, these external stores (databases, files, etc.) are considered first-class context providers.
Notifications play a key role in coordination and memory. MCP servers can broadcast updates to all connected agents whenever their data changes. For instance, a shared database server might alert clients each time a new record is added, prompting agents to reload the pertinent information. In this way, even parallel agents maintain a common understanding of the shared data.
Consider a collaborative research use case to illustrate these concepts. Suppose a question like “Summarize the latest findings on AI safety” is sent to a Lead Researcher bot. The lead agent connects to a Memory service, a Document Search service, and a Citation service via MCP. To avoid losing context, the lead agent immediately writes its planned strategy (for example, breaking the topic into subtopics) into the memory store.
Next, the lead agent spawns several specialized sub-agents, each handling a different task (such as “search recent conference papers,” “extract key points,” or “compile citations”). Although they all use the same MCP servers, each sub-agent runs its own MCP client. For example, one “search” sub-agent might use the Document Search server’s API tool to fetch papers, then use a summary tool on those documents. It writes its results back to the memory server as it goes. Meanwhile, a “citation” sub-agent could take the collected information and use a citation-matching tool on the Citation server to find sources for each assertion.
All agents communicate using MCP-defined primitives. They treat the common memory, search APIs, and other services as standardized tools and resources. Sub-agents can notify others when they finish tasks and have new data available. For instance, the Memory server might signal the lead agent that a batch of summaries is ready from one sub-agent. After the lead agent retrieves and integrates these results, it can initiate more subtasks. Throughout this process, no agent needs to know the internal details of another agent or server—every component simply speaks the MCP protocol.
PIT Solutions leverages the power of MCP to design and implement robust, collaborative multi-agent systems. Our team builds custom MCP-based architectures that allow diverse AI tools to plug into a shared context bus seamlessly. This means your agents can share knowledge and coordinate tasks without losing critical information. We also provide expertise in integrating new data sources and tools into the MCP ecosystem, ensuring your system remains flexible and scalable. By partnering with PIT Solutions, organizations can harness MCP to streamline complex workflows, accelerate innovation, and reduce the engineering effort required for multi-agent coordination. Contact PIT Solutions to integrate MCP into your AI workflows
