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Tag: MCP

Context on Tap: How MCP Servers Bridge AI Agents and DevOps Pipelines

Large language models can draft code or move artifacts, but without situational awareness they still trip over the basics. Cloudsmith CEO Glenn Weinstein tells Mike Vizard why a new piece of plumbing—the Model Context Protocol (MCP) server—is quickly becoming table stakes. Think of MCP as a receptionist for AI agents: it answers questions like “Which Docker images are in my repo?” and supplies environment-specific details the model would otherwise guess—or miss entirely.

Context alone isn’t enough. Developers increasingly chain agents together to run multi-step jobs—pull a package, scan it, publish it—without a human in the loop. That hand-off requires agent-to-agent (A2A) protocols so one bot can call another securely and without endless re-authentication. Google’s recent move to donate A2A to the Linux Foundation hints at how fast the ecosystem is converging on open standards.

More context and more agents lead to more builds—sometimes hundreds per day. Weinstein warns that existing CI/CD pipelines will bottleneck if artifact storage can’t keep up. Teams already accustomed to nightly releases will feel pain when AI turns “once a day” into “once an hour” unless repositories serve packages globally and caches stay warm.

There’s also a supply-chain angle. Hallucinating agents may suggest outdated or even nonexistent packages. An artifact manager that doubles as a control plane—tracking provenance, scanning for vulnerabilities and rejecting spoofed names—becomes a final checkpoint before code reaches production.

Weinstein’s takeaway is blunt: experiment with AI copilots today, but raise expectations for every tool in your stack. If a platform can’t expose its data through an MCP endpoint and play nicely with agents, it will feel ancient in a year. Start mapping where context lives, audit your APIs and assume that the next generation of developers will treat AI companions as a given. Your pipelines should be ready before they arrive.

August 4, 2025
Coralogix MCP Server Offers Observability View Into AI Agents

Full-stack observability platform company Coralogix has detailed the launch of its new Model Context Protocol Server technology. The product is designed to allow third-party AI agents to connect directly to Coralogix’s observability data services to provide a deeper view into the new fabric of agentic AI connections.

Breaking down the observability viewfinder on offer here, Coralogix’s perspective purview covers logs, metrics and traces and extends to formalized security information and event management (SIEM) controls.

Newly Barrelled RUM

Additionally, the company has a wide-angle view on real user monitoring (pleasingly shortened to RUM), which we can define as performance monitoring focused on actual user clicks, inputs and other interactions (with no synthetic data incorporated, as is often the way) with an application, website or data service.

As agentic AI now becomes ubiquitously promoted and promulgated (if not actually always deployed), RUM observability enables developers and operations staff to identify bottlenecks and optimize performance. Crucially, it also offers a deeper view into how new agentic services are being used to help assess their worth.

Coralogix’s MCP Server applies to observability management across live production environments, but also extends “backwards” through application and service production, staging, prototyping, debugging and what the company loosely defines as “other environments” as well.

The new service is hoped to help organizations deploy more efficient AI agents through access to detailed observability data, which should help reduce mean time to resolution (MTTR) when errors occur. Coralogix also says it will help in terms of streamlining agent workflows and minimizing engineering overhead.

What MCP Means For DevOps

Speaking directly to devopsy.kinsta.cloud in line with his firm’s new launch, Liran Hason, VP of AI for Coralogix, said that, “MCP fundamentally shifts DevOps by embedding AI directly into the fabric of observability, turning raw telemetry into actionable intelligence in real time. This empowers teams to move from reactive firefighting to proactive system management, automating diagnostics, speeding up incident resolution and enabling continuous reliability at scale. This interaction ultimately helps train AI agents, enhancing their capabilities and effectiveness.”

As readers will know, MCP is an open standard developed by Anthropic, the company behind Claude large language model and AI chatbot. In increasingly widespread use at the time of writing, MCP provides a way to connect tools, data and services to AI models and systems. Through the Coralogix MCP Server, AI agents can directly access detailed information about a customer’s applications and infrastructure.

Last quarter, Coralogix introduced Olly, its AI observability assistant. Olly is a site reliability engineering (SRE) agent that can analyze production systems, understand the full context of logs, metrics and traces. It also helps surface root cause analysis (RCA) and business impact.

A Secure MCP Developer Endpoint

Onward from the chirpily-named Olly, the company says that the more formally-labelled Coralogix MCP Server is more aligned towards providing context to builders, programmers and software application developers. It works by offering a secure MCP endpoint so developers can stream live telemetry into their own AI agents, integrated development environments (IDEs), or chat-ops workflows to then help shape user experiences to suit.

“Agents generally lack direct access to specific observability data, which limits the AI’s utility for this purpose. What makes Coralogix’s MCP Server unique is its ability to surface observability data that is highly specific to each customer. It can search through data to find custom attributes and entities that reflect the customer’s unique setup, leading to more accurate results when AI agents access logs, metrics and traces. Customers can also use natural language prompts to locate key metrics or events,” said Coralogix’s Hason.

By integrating with tools developers already use, such as the widely used AI code editor Cursor or IDEs, the MCP Server enables AI agents to not only detect issues in real-time but also assist in diagnosing and resolving them all within the same workflow. Hason and team call this a “closing the loop capability” that streamlines operations and reduces the need to switch between multiple tools.

July 16, 2025
AWS Extends MCP Support in Amazon Q Developer to Multiple IDEs

Amazon Web Services (AWS) has added support for the Model Context Protocol (MCP) with the Visual Studio Code and JetBrains plugins that are provided for Amazon Q Developer, a set of artificial intelligence (AI) agents that automate a range of software development tasks.

Originally developed by Anthropic, MCP support with these integrated development environments (IDEs) will make it simpler to invoke external tools that invoke some type of large language model (LLM).

Srini Iragavarapu, director of software development for Amazon Q Developer Coding Assistant, said this capability will make it easier to provide AI agents with additional context by providing access to additional data sources and application programming interfaces (APIs). Armed with those insights, it then becomes a lot simpler for developers to optimize the prompts they create to invoke Amazon Q Developer, he added.

That capability, in addition to creating more accurate code, will also make it simpler to integrate planning tools, create user interface components from designs and generate database documentation from actual schema without having to create additional custom integration code, noted Iragavarapu.

Previously, AWS only provided that capability via MCP support for the command line interface (CLI) option it makes available for Amazon Q Developer.

Over time, multiple classes of AI agents will emerge that provide different levels of reasoning capabilities that will be assigned tasks based on the level of complexity involved, said Iragavarapu. AWS also continues to evaluate the agent-to-agent (A2A) protocol put forward by Google to make it simpler for AI agents to communicate over a standard set of interfaces, noted Iragavarapu.

It’s not clear how widely AI coding tools have been adopted a Futurum Research survey noted, but found 41% of respondents expect generative AI tools and platforms will be used to generate, review and test code.

There is, of course, already no shortage of AI coding tools, so the pace at which code is being developed has already begun to accelerate. Exactly how much of that code is making it into production environments is unknown. However, the next major challenge will be incorporating AI agents into the software engineering workflows used to deploy applications. Many of the DevOps pipelines relied on today to deploy software were not designed to handle the volume of code being generated by AI tools. The expectation is that many of the brittle scripts that DevOps engineers rely on today to create pipelines will eventually be replaced by AI agents that can perform tasks at higher levels of scale.

In the meantime, each organization will need to determine to what degree they may want to standardize on a specific AI tool versus letting each developer select one based on their personal preference. The important thing is to remind developers to review the code created by AI coding tools that, on occasion, are still prone to hallucinations. After all, even if a machine wrote the code, it’s still a human developer who is ultimately responsible for the quality of the code being created.

June 19, 2025
CloudBees Adds MCP Server to Unify Platform for Integrating DevOps Workflows

CloudBees today began providing early access Model Context Protocol Server to its Unify platform for centrally managing DevOps workflows across multiple types of continuous integration/continuous delivery (CI/CD) platforms.

Originally developed by Anthropic, MCP has emerged as a de facto standard for integrating artificial intelligence agents with each other and various data sources.

Shawn Ahmed, chief product officer for CloudBees, said MCP will enable CloudBees to extend the reach of its Unify platform to AI agents that are becoming incorporated into DevOps workflows. The CloudBees MCP Server provides a lightweight, standardized interface between large language models (LLMs), such as OpenAI’s GPT and Anthropic’s Claude, and enterprise DevOps tools and processes, he added.

The overall goal is to make it simpler to orchestrate and automate multi-step tasks spanning multiple AI agents by making pipeline, test, security data and associated metadata, including metrics, available to AI agents via the CloudBees Unify platform, noted Ahmed.

Launched last month, CloudBees Unify is a software-as-a-service (SaaS) platform for centralizing the management of multiple DevOps environments, including GitHub, GitLab and the open source Jenkins continuous integration/continuous delivery (CI/CD) platforms. It enables DevOps and platform engineering teams to centrally manage application development environments more easily via a control plane for CI/CD platforms through which DevOps teams can govern and manage multiple platforms.

In effect, CloudBees Unify provides an operating layer on top of any existing toolchain that makes it possible to both surface analytics in real time, run tests and apply policies that require the running code scans and implement compliance policies. That capability can now also be extended to AI agents to ensure governance policies are followed as tasks are automated, noted Ahmed.

It’s not clear how widely AI agents are being deployed across DevOps workflows, but a Futurum Group survey found 41% of respondents expect generative AI tools and platforms will be used to generate, review and test code. The challenge now is determining to what level tasks can be assigned to AI agents in a way that will be reliably implemented. The issue isn’t so much whether AI agents will be used as much as it is to what degree. Additionally, DevOps teams will then need to determine how potentially hundreds of AI agents might be employed alongside the engineers, who, in addition to continuing to perform some tasks themselves, may need to manage a small army of AI agents.

Ultimately, each DevOps team will need to determine their level of comfort with AI, but the one certain thing is that there is no shortage of manual DevOps tasks to be automated. It’s not likely AI agents will replace the need for software engineers but the nature of the role will undoubtedly change, especially as AI agents increasingly eliminate the need to write custom scripts.

In the meantime, however, DevOps teams would be well-advised to revisit how their existing pipelines are constructed. After all, the more brittle those pipelines are, the more likely it becomes that AI agents will stress them to the point of breaking.

June 18, 2025
New Relic Adds Support for Model Context Protocol to Observability Platform

New Relic this week added support for the Model Context Protocol (MCP) to its observability platform to surface insights into artificial intelligence (AI) agents and applications.

Originally developed by Anthropic, MCP is rapidly becoming a de facto application programming interface (API) for enabling interoperability between AI agents and other sources of data.

New Relic has now incorporated MCP with its application performance monitoring to surface insights into AI agents and applications alongside its ability to monitor and observe legacy applications.

Previously, New Relic had added an ability to observe large language models (LLMs), but via MCP it’s now able to extend the reach of that capability to AI agents and applications that access those LLMs. That capability makes it possible to visualize specific usage and patterns of the entire lifecycle of an MCP request, including invoked tools, call sequences and execution durations.

Additionally, DevOps teams can identify which prompts are being invoked and what choices were made by an AI agent along with surfacing latency, errors and other MCP performance issues.

New Relic AI Monitoring MCP support is now available in Python Agent version 10.13.0, with additional languages coming soon.

In general, New Relic reports it has seen a steady 30% growth in usage of its AI monitoring tools quarter-over-quarter for the past year. The company has also seen a 92% increase in the number of unique AI models being used. However, the ChatGPT models provided by OpenAI accounted for 86% of all the LLM tokens being generated.

Nic Benders, chief technical strategist for New Relic, said that level of activity suggests that OpenAI is well on its way to becoming a de facto standard, even as the number of AI models being made available continues to rapidly increase.

It’s not immediately clear why one LLM might be preferred over another but OpenAI, in addition to making a steady stream of investments, has successfully generated a significant amount of brand recognition. That helps make OpenAI one of the first platforms that most organizations are going to look to when building an AI application, said Benders.

However, there are use cases, such as coding, that lend themselves, for the moment at least, better to coding, he added.

Less clear is the degree to which the various AI models that are available will gain enough traction to warrant the ongoing level of investment required to sustain them. IT teams should carefully consider what foundations they want to build and deploy AI applications on for the long term, noted Benders.

Additionally, not every AI application requires the latest, and most expensive, LLM, said Benders. The cost of previous generations of AI models has declined considerably as more advanced models have become available. Many applications, however, may not require access to a more advanced model, especially when a previous generation will enable a developer to automate a task at a much lower cost.

Ultimately, observability platforms will play a critical role in enabling organizations to track which AI models are being used for which use cases. The challenge now is understanding not only which ones are gaining traction among application developers, but just as importantly, the symbiotic relationship that exists between AI models and the IT infrastructure upon which they depend.

June 13, 2025
Harness Adds MCP Server to Expose Data to Third-Party AI Tools

Harness today revealed it has developed a model context protocol (MCP) server through which it will be able to provide more context to artificial intelligence (AI) tools, agents and platforms that are being used to build software.

Rohan Gupta, a principal product manager for continuous delivery and AI DevOps for Harness, said exposing the data residing in the Harness continuous integration/continuous delivery (CI/CD) platform will streamline workflows by eliminating the need to rely on custom plug-ins and application programming interfaces (APIs) to provide additional context about the application development environments where code is ultimately deployed.

Originally developed by Anthropic, MCP is based on a JSON-RPC 2.0 API framework and is rapidly becoming a de facto API standard for integrating AI tools, agents and platforms. The overall goal is to make it easier to expose relevant data to any AI agent that has permission to access it. The overall goal is to make it simpler for AI coding tools to more easily understand the reasons why, for example, a specific DevOps pipeline might have failed, said Gupta.

DevOps teams will also be able to employ the role-based access controls (RBAC) provided by Harness to limit access to deployment logs only. All the API keys are managed through Harness and no data is ever sent to a large language model (LLM).

It’s not clear how widely AI tools and platforms have been adopted but a Futurum Group survey finds 41% of respondents expect AI tools and platforms will be used to generate, review and test code, while 39% plan to make use of AI models based on machine learning algorithms.

Ultimately, DevOps engineers will soon find themselves supervising a small army of AI agents with the help of what are becoming known as Planner agents, said Gupta. It remains to be seen how multiple AI agents will interact with one another to complete an end-to-end task but as orchestration frameworks continue to evolve there will be higher levels of interoperability between agents that both Harness and its third-party partners are continuing to build, said Gupta.

In the meantime, DevOps teams might want to start creating an inventory of manual tasks that might soon be assigned to an AI agent. As the ability of these AI agents to reliably complete these tasks improves, DevOps engineers should be able to spend more of their time on activities that provide higher value to the business, he noted.

Arguably, the next major challenge will be the cultural changes that will inevitably occur as more DevOps tasks are automated using AI agents that, for all intents and purposes, are now part of a DevOps team, added Gupta.

Regardless of approach to building software, the expectation is that the data that AI agents need to write better code will be readily made available. Each organization will need to decide to what degree to trust AI technologies to build software themselves, but ultimately, as the pace of software innovation starts to accelerate in the age of AI, it may soon not be feasible to effectively compete without it.

May 29, 2025
AWS Adds MCP Support to Amazon Q Developer Platform

Amazon Web Services (AWS) this week added support for the emerging Model Context Protocol (MCP) to Amazon Q Developer, a suite of artificial intelligence (AI) agents for application developers that can now be more easily integrated with other AI tools and data sources.

Adnan Ijaz, director of product management for developer agents and experiences at AWS, said support for MCP in the command line interface (CLI) that AWS makes available to developers to access any MCP server. That capability will also soon be extended to the integrated development environment (IDE) that AWS provides for Amazon Q Developer.

Originally developed by Anthropic, MCP enables two-way connections between data sources and AI tools. Cybersecurity and IT teams can both expose data through MCP servers or build AI applications, also known as MCP clients, that connect to these servers. That approach makes it possible to query internal systems without blindly scraping data or exposing backend systems. In effect, an MCP server acts as an intelligent gateway, translating natural language prompts into authorized, structured queries.

For example, developers can via MCP in addition to describing AWS, also describe database schemas to build an application without having to directly invoke a specific variant of SQL or write any Java code that might be required.

The ultimate goal is to reduce the current level of bespoke connectors that would otherwise be needed to achieve the same level of integration, said Ijaz.

Exactly how much AI agents are now being used to build software is unclear, but a Futurum Research survey finds 41% of respondents now expect generative AI tools and platforms will be used to generate, review and test code. The one certain thing is that the volume of code being created is going to continue to increase exponentially in the months and years ahead as AI agents are integrated into software engineering workflows.

Each organization will, as a result, need to determine for itself to what degree to rely on AI agents to build and deploy applications. The quality of the code being generated by AI tools can vary widely, and in many instances, organizations are hesitant to deploy code in production environments unless a human developer thoroughly understands how it was constructed.

Nevertheless, as AI coding continues to evolve, the productivity gains are too much to ignore. Application development teams, at the very least, should by now be experimenting with various approaches, especially as it becomes simpler to combine multiple tools to build the next generation of AI-infused applications.

In the meantime, the pace of AI innovation is only going to accelerate. Before too long, the code surfaced by AI tools will steadily improve. DevOps teams will soon find themselves building, deploying and updating a wide range of applications at levels of scale that a few short years ago might have seemed unimaginable.

The issue now will be determining to what degree the pipelines currently used to drive the workflows relied on to manage DevOps processes might need to be revamped to accommodate all that code.

May 2, 2025