There’s no denying the excitement around Model Context Protocol (MCP), an open protocol for connecting AI assistants with external data, tools, and APIs. Since its debut by Anthropic in late 2024, thousands of MCP servers have emerged for devops, cloud, and beyond.

Now that developers have integrated MCP servers into applications, and they have been battle-tested, usage patterns are emerging. For instance, supplying better context for AI is the most commonly cited primary value of using MCP, according to Zuplo’s State of MCP report released in early 2026. The Zuplo report also found that 63% of MCP users adopt MCP servers for accessing data sources such as documentation or knowledge bases.

In software development, context engineering is the act of supplying AI coding agents with relevant data and capabilities to improve the accuracy and relevance of their outputs. It also involves optimizing the breadth of information to guide efficient processing. Such context can include coding style, internal libraries, institutional knowledge, production data, and external data from platforms like Slack, Atlassian, Notion, or GitHub, among others.

“MCPs support context engineering because it creates a standard way for AI systems to connect to various business tools,” says Todd Olson, CEO of Pendo, a product experience platform. “The key benefit is that the agent determines what context it needs based on the question, then uses the appropriate MCP server to fetch that information in real time.”

With the rise in AI-assisted coding, MCP is becoming a doorway for real-time dynamic search and retrieval across various sources, playing an important role in context engineering efforts. As Joey Stout, solutions architect at Spacelift, an infrastructure orchestration platform, puts it, MCP is the “saving grace of vibe coding.”

How MCP boosts context engineering

Using MCP, agents can fetch structured data contextually relevant to the task at hand. According to Edgar Kussberg, group product manager at Sonar, MCP accelerates the knowledge-hunting engineers must routinely perform on a daily basis.

“When an engineer needs to answer a question, they do not rely on memory alone,” says Kussberg. “They navigate code repositories, dashboards, CI systems, documentation, and security reports, pulling information from each system as needed. MCP gives AI agents that same capability.”

Many of the most popular MCP servers retrieve contextual information to improve agentic coding. For example, an MCP server from Context7 provides up-to-date documentation, while another from Filesystem pulls from any directory on a local machine. An MCP server from Sentry accesses production issues and errors, a server from SonarQube exposes security issues, and a server from Multiplayer returns user session data.

The great thing about using MCP for these situations is that it avoids the need to put large code chunks in every prompt. Instead, coding context like relevant methods, dependencies, or recent changes can be called at runtime, says Venugopal Jidigam, head of agentic platform engineering at WaveMaker, an agentic development platform. “The MCP server assembles and returns scoped, structured context, which the model then uses to reason and respond accurately,” he says.

Another common context-gathering example is retrieving institutional knowledge. “Instead of hardcoding that knowledge into the model, the agent uses MCP to retrieve relevant documents or data at runtime,” says Ebrahim Alareqi, principal machine learning engineer at Incorta, a data and analytics platform provider. “This keeps the agent lightweight while still giving it access to enterprise-specific context when needed.”

Others praise MCP for its role in bringing common standards to agentic data retrieval. “MCP provides the plumbing that makes context engineering practical,” says Gil Feig, co-founder and CTO at Merge, an API platform provider. Without standards, teams end up building fragile custom data pipelines that break often, he adds.

Benefits of using MCP for gathering context

AI-assisted coding has some challenges. Most notably is a trust issue. The vast majority of developers don’t trust the output of AI coding agents — 96%, according to Sonar’s 2026 State of Code Developer Survey report. A second challenge is increased time spent reviewing and debugging AI-generated code. A late 2025 StackOverflow survey found nearly half of developers report frustration dealing with AI solutions that are “almost right, but not quite”.

Context engineering, as well as the use of MCP for this purpose, could help overcome many of these challenges. Using MCP servers, engineers can automatically append relevant logs or internal data to their prompts, refining LLM processing considerably to avoid irrelevant outputs.

The end result is improved accuracy. “MCP allows systems to dynamically fetch what the model needs, like APIs, databases, files, or domain knowledge,” says Neeraj Abhyankar, VP of data and AI at R Systems, a digital product engineering company. “This makes prompts leaner, reduces hallucinations, and ensures models operate with task‑relevant context.”

Another huge benefit is better context window management. Using MCP for context engineering can enable more efficient interaction with underlying models. “MCP tools can save you thousands of tokens just by ensuring you’re using the right things,” says Spacelift’s Stout.

Stout specifically highlights the GitHub MCP server. “It can now access specific files directly from GitHub and do GitHub searching and all the bells and whistles you expect when referencing GitHub,” he says. “MCP made retrieval from GitHub a million times better.”

Using MCP also enhances autonomy and scalability across an enterprise. “Teams can stop relying on partial views or anecdotal evidence and instead operate from a shared understanding,” says Pendo’s Olson. This greatly reduces the friction typically involved in stitching tools, building reports, or looping in teammates, he says.

All in all, the experts say the benefits of MCP in context engineering are numerous. Standardizing on MCP affords more focused prompts that generate more explicitly and relevant context, decreasing the likelihood of LLM hallucination and optimizing what the agent acts on. This in turn can lessen the manual review required for validation and debugging, reclaiming some developer time in the process.

Together, these benefits aim to solve many of the core issues inherent in AI-generated code and agentic workflows at large. “MCP shifts AI development from fragile prompt tuning to repeatable engineering,” says WaveMaker’s Jidigam. “The result is consistent behavior, minimal data exposure, and AI systems that can scale.”

To MCP, or not to MCP

Experts agree MCP can go beyond retrieval augmented generation (RAG) to provide more timely and relevant content in a more optimized fashion. “Traditional knowledge bases and RAG pipelines rely on pre-indexed snapshots,” says Sonar’s Kussberg. “In fast-moving environments, this quickly becomes outdated.”

For this reason and others, MCP unlocks all kinds of possibilities for developers. That said, the protocol is not a silver bullet for all use cases. It’s up against competing agentic protocols for some scenarios, and even simple CLI or direct API access for others.

Ballooning portfolios of MCP servers can increase LLM inputs substantially, too, requiring vigilant optimization techniques to avoid hitting token limits. Such strategies include intentionally designing tools, progressive disclosure, automated discovery, and other emerging tactics.

Then, there are MCP-related security concerns. The security model for the MCP protocol itself has matured quite a bit, but it’s incumbent upon implementers to enforce the correct permissions. “MCP, when implemented the right way, lets you enforce policy-driven access controls,” says Merge’s Feig. This should prevent a junior engineer, for instance, from accessing logs they’re not authorized to access, even if the agent has broader permissions, he adds.

To boost confidence using MCP within enterprise development settings, many experts recommend using an MCP registry that houses vetted, governed MCP servers approved for internal use. Other tools and practices, including agent skills, code mode, and emerging specifications for deterministic AI also promise to play a role in establishing context for agents.

Beyond MCP itself, Stout recommends using Claude Code’s tool search feature, which searches for tools without using token windows. He also highlights Sisyphus, an OpenCode-compatible agent for matching models with different tasks, and Plannotator, a plugin for Claude Code and OpenCode that can be used to plan projects, both of which can aid optimization.

Context is king

The pace of MCP development is accelerating. Analysis of 1,400 MCP servers by Bloomberry charted a 232% increase in six months, from August 2025 to February 2026. Interestingly, read operations outpaced write operations two to one, indicating these servers are performing a significant amount of data retrieval.

Looking to the future, context engineering is anticipated to continue cementing itself as a software discipline, while MCP wields the power to transform APIs into engines for agentic reasoning. As Jidigam says, “MCP-like abstractions will become standard infrastructure, much like REST did in earlier eras.”

Others are similarly confident. “In context engineering, MCP becomes the control plane agents use to access context, tools, and actions,” adds Incorta’s Alareqi. “It will be foundational as software becomes increasingly agent-driven.”

The underlying takeaway: MCP already plays a large role in context engineering, and will continue to do so. As the standard interface between AI systems and data, MCP is the primary vessel for dynamic cross-platform context retrieval at run time, allowing engineers to fetch documentation, API references, policies, available actions, and more.

However, this doesn’t mean context engineering has reached its zenith. Looking ahead, context engineering will evolve from fetching information to coordinating it, says Kussberg, combining multiple MCPs along the way. This will require increased discipline in enforcing standards, assessing risks, and validating changes more often, he says.

So, get started with context engineering using MCP, and stay alert to how your MCP servers are impacting LLM token usage and shaping workflows. The difference between context and non-context matters. Because, as they say, context is king.