As AI agent ecosystems mature, the need for robust monitoring and observability has moved from “nice to have” to non-negotiable. With emerging standards like Anthropic’s Model Context Protocol (MCP) introducing context-based access to models, developers, and operators are gaining finer-grained control over agent workflows and interactions.

With this increased control, however, comes the need to monitor these systems. Poor monitoring in the MCP ecosystem could introduce significant issues ranging from data leakage to undetected runaway agent drift. Accordingly, simply building out an MCP isn’t enough – you need to understand and build the proper observation systems as well.

Today, we’re going to share some tips for monitoring MCP ecosystems. We’ll look at the metrics you should be observing and some approaches to deploying this strategy at scale.

What Should You Be Observing?

While traditional API observability focused on latency, throughput, and error rates, agentic systems require a much deeper and more nuanced view — one that incorporates the state, context, and behavior of the agent over time.

Let’s look at some specific elements you should be observing.

Context Lifecycle Metrics

These metrics are all about your context usage throughout your agentic implementation. These include:

• Context length and token usage per request/session
• Context reuse vs regeneration rates
• Staleness detection, such as the time since the last context refresh
• Mutation rates, which indicate how often context is rewritten or updated

Understanding how agents use, manage, and reuse context is critical for ensuring high performance, compliance, and data retention alignment, but can also be hugely effective in optimizing flows and context sharing.

Agent Behavior and Decision Pathing

Metrics of this type should track not only how an agent interacts with the requests and the data they touch, but also how the decisions on their actions were made. This data includes:

• Intent-to-action traceability to map the prompt to the eventual API calls
• The whole chain of delegation: if an agent delegates a subtask to another agent or model, you should be able to track the call hierarchy to its ultimate endpoint
• Divergence from expected behavior, measuring deviation from pre-defined or trained workflows

Gathering this data requires integrating with internal instrumentation or wrapping agent calls in structured metadata to observe reasoning steps. Although it takes effort to set up, it’s a key step towards ensuring you understand how the agents are working within your ecosystem. With MCP systems, it’s as much about validating the connections between elements as it is about ensuring the agents are using those connections properly, so this is a huge step to that end.

Security and Access Patterns

MCP has some significant security concerns, and as such, you need to step up your metric observability to ensure that you are deploying security and access pattern tracking. This includes tracking:

• Credential use per agent/task, especially when transferring between services or data stores
• Unexpected role escalation or permission creep
• Sensitive endpoint access and payload audit trails
• Rate spikes and burst access patterns suggesting abuse or runaway agents

This ties into agentic API security best practices like identity-bound tokens, dynamic policy enforcement, and behavior-based access control, but with MCP servers, can also serve as a sort of canary in the AI mine. Not every issue within an MCP ecosystem is going to be defined by malicious actors — some of the worst damage you can endure will come from the system itself. Accordingly, you should track the agentic behavior to make sure the contracts and security systems are being effectively adhered to.

System-Level Metrics

Of course, we’d be remiss if we didn’t mention the system-level metrics that you should be collecting. These are useful across the board for everything from optimization to business validation. Since you are collecting metrics across the board, it only makes sense to collect the base metrics as well. These include:

• Latency variance by agent type
• Task resolution time, broken down by API calls, retries, and fallback invocations
• Prompt complexity scores, which serve as a predictor for resource contention or hallucination risk
• Token economy metrics, or the ratio of useful tokens (contextually relevant) to wasted tokens, can help identify issues with processing and functionality

Observability here should blend structured metrics with unstructured telemetry like logs and trace spans. By looking at the whole picture, you can get an idea of how the system works in context and in combination with all the associated data stores and systems.

MCP Vulnerabilities in the Wild

While the MCP field is still quite young, we’ve already seen some of the results of not monitoring MCP ecosystems or implementing secure best practices. These present the best argument for what happens when you don’t have good monitoring and observability, so they bear consideration here.

Our first case comes from Anthropic itself. On July 1st, 2025, a critical vulnerability in Anthropic’s MCP Inspector dev tool (CVE-2025-49596) was detected, allowing unauthenticated attackers to execute arbitrary code on a developer’s machine just by getting them to visit a malicious site. The issue stemmed from how MCP Inspector exposed its local HTTP interface without authentication, combined with a browser-based CSRF vector. The moment a developer opened a compromised site while running the Inspector tool, an attacker could silently issue tool load or execution commands via MCP. This highlights a broader risk in local MCP tooling: without explicit binding and hard auth boundaries, even developer-side utilities can become remote entry points.

Another example comes from Asana. On June 4th, 2025, a data exposure bug was detected via its MCP Server. The bug seems to have come from its authentication and endpoint routing, ultimately exposing data including task-level details, project metadata, team information, comments and discussions, and uploaded files for more than 1,000 customers. While the exposure could have been worse with many more impacted, Asana reacted quickly to fix the problem.

These issues are unfortunately growing in commonality: a recent report by Backslash notes that core problems can be detected on hundreds of public MCP servers, saying:

"The Backslash team analyzed thousands of publicly available MCP servers. At the time of writing, we covered about half of what is available, and this is an ongoing effort. We scanned MCPs for code vulnerabilities and other weaknesses, and also checked them for malicious patterns [...] While our analysis did not yield obviously malicious MCPs, we did find a startling number of dangerously misconfigured or carelessly built servers."

This is not a theoretical problem — it’s currently resulting in massive security vulnerabilities that just aren’t being effectively addressed. We’ve seen WhatsApp experience a major MCP data exposure, GitHub’s MCP server open the door for potential prompt injection, and much more. The problem is only going to get worse the more ubiquitous this tech becomes.

These issues largely stem from some basic observability and monitoring failures. For instance, there’s no reason that hundreds of MCP servers found by Backslash could have MCP servers explicitly bound to all network interfaces via 0.0.0.0. This is simply an issue of not paying attention, not deploying the right systems, and not taking this seriously.

Outcomes of Better Monitoring and Observability

While this all seems pretty heavy, better monitoring and observability directly benefit your services in a few significant ways. For instance, doing this right can catch hallucinated API calls before they hit production systems, or diagnose edge-case failures that traditional monitoring would miss (such as silent token truncation)

Monitoring and observability at this level could help enforce guardrails and policies based on actual agent behavior rather than assumptions, which could be used to improve performance by reducing unnecessary context propagation or redundant API calls. Better oversight also assists in conducting post-mortems with full replayable traces, even for multi-agent workflows.

Ultimately, better observability leads to more reliable agent behavior, safer API integrations, and faster iteration cycles. Put another way, this is worth doing — and getting right.

How to Implement Monitoring Effectively

With all of this in mind, MCP ecosystems have a few different approaches that can be taken for effective monitoring.

Instrumentation

One good method for monitoring is using instrumentation. In essence, this approach uses middleware to connect the various requests and systems and route the logs, models, and data flows into ingest systems such as context provenance graphs, heuristic analysis, and even agentic context mining.

This kind of instrumentation is necessarily complex, but it does handle the interplay between different pieces quite well. Unfortunately, it does often require specific services or servers set up to govern these systems. While solutions like OpenAPI are great for less complex APIs, these multi-agent systems can grow so complex with instrumentation that the management of telemetry and data can become almost as heavy as the application itself.

Middleware Gateways

One solution that has become quite popular as of late is to simply adopt what the API space has been using for years — a gateway. AI gateways are custom-built middleware systems that sit in the middle of all data interactions. Unlike instrumentation, which allows each service to effectively generate its own logging, gateways ingest all the data and generate their own reports.

This does mean that such reporting is really only as high quality as the testing and systems developed to ingest it, but there are many good systems for doing this. One good example is the Kong Gateway, which routes all data through a central service that can then be used to govern the services and generate useful monitoring reports across the board.

This, of course, does lock you into a single point of failure while reducing the complexity of other solutions, so you must consider which trade-off you prefer.

Infrastructure Monitoring

In some cases, you can directly monitor your MCP services if you are running local resources or infrastructure. In these scenarios, you are only going to get so much data, but that may be enough for your given use case. For example, if you are a provider offering simple services such as multi-platform authentication, you may not need to care as much about prompt efficiency as you need to care about the actual resources used in hashing and securing the authentication tokens. In these cases, you can simply look at the infrastructure use and gauge efficacy that way.

It does need to be mentioned that this is by far the most general and haphazard way of collecting metrics. It really only applies to a specific subset of use cases, and is much more reporting symptoms than actual practical use. Nonetheless, it is a valid option, and one that should be mentioned.

Final Thoughts

AI systems aren’t going anywhere, and the need to secure these ever-evolving complex solutions necessitates a rethinking of how, what, and when we collect and use data. By collecting the data we’ve laid out in this article, you can start to effectively monitor your systems.

Do keep in mind, however, that this is an evolving topic, and as this cutting-edge technology evolves, so too will the data you collect — and how you collect it.