Leverage API7-MCP for Simplified API Monitoring

Introduction

In the era of microservices and API economies, API gateways serve as critical traffic entry points, making their performance and stability of utmost importance. Traditional monitoring methods often rely on complex dashboards, querying languages (such as PromQL), and scattered management interfaces. These not only have a high learning curve but are also inefficient for quickly pinpointing issues and obtaining comprehensive information.

API7-MCP aims to break down these barriers. By leveraging the MCP protocol to integrate API7 Enterprise metrics with AI, it allows users to interact with API7 Enterprise using natural language. This enables a range of operations including metric queries, alarm analysis, and contact management, significantly simplifying API monitoring and operations.

This article delves into how to utilize API7-MCP to retrieve and analyze monitoring metrics through natural language.

What API Monitoring and Analysis Capabilities Does API7-MCP Offer?

Built on the capabilities of MCP, API7-MCP can query monitoring data such as gateway traffic, gateway group/instance/service/route monitoring.

The features of API7-MCP can be applied in several practical scenarios:

1. Daily Operations Inspection: Operations engineers can quickly understand the health of gateway clusters and core services through simple natural language commands, such as "What was the gateway traffic peak this morning?" or "Is the error rate of the core service normal?".

2. Rapid Troubleshooting: When receiving alerts or user feedback, API7-MCP can be swiftly used to query real-time metrics (latency, error rate) of related services, enabling corresponding actions and forming a rapid response loop.

3. Performance Analysis and Optimization: Developers and operations engineers can easily query performance metrics of specific services or routes over different periods, such as "Compare the average latency of service C between last week and this week", providing data support for locating and optimizing performance bottlenecks.

4. Team Collaboration and Information Synchronization: Team members (even those without a technical background) can query and understand API operations through natural language, facilitating cross-departmental communication and information synchronization.

How to Use API7-MCP for API Monitoring and Analysis?

1. Querying Monitoring Data of Gateway Groups

With API7-MCP, users no longer need to manually write complex PromQL queries to access comprehensive monitoring metrics for gateway groups.

For example, to retrieve monitoring data for the default gateway group over the past 10 minutes, the AI Client uses the get_prometheus_metrics tool to automatically collect all relevant metrics. These include status code distribution, number of failed requests, total API request count, bandwidth usage, request latency, connection status, and queries per second (QPS), etc.

The AI Client can also give us organized data by category. If needed, it can analyze the data and provide further optimization suggestions.

2. Querying Monitoring Data of a Specific Service within a Designated Gateway Group

Moreover, API7-MCP supports fine-grained monitoring. In addition to viewing aggregated data of the entire gateway group, we can also focus on a specific service. For instance, we can query the operational status of the httpbin service under the default gateway group over the past 5 minutes, including:

• Status code distribution of a single service to clearly identify service response quality;

• Request latency performance of the service to detect any trends of response timeouts;

• Sudden request surges, error spikes, and other abnormal fluctuations within a specific time period.

This is particularly crucial for troubleshooting. For example, a high proportion of 429 errors may indicate insufficient rate-limiting configuration or potential attack behavior, while frequent 502/503 errors might suggest service instance downtime or unstable upstream connections.

By refining query dimensions, operations and development personnel can make decisions more efficiently and achieve precise governance.

Conclusion

Users can monitor various metrics intuitively via the visulized API7 Enterprise console and can also integrate AI capabilities through API7-MCP for intelligent monitoring and data analysis of API7 Enterprise. Using both methods together meets the real-time visualization needs for daily monitoring and enables quick identification of complex issues through intelligent analysis, offering users a more comprehensive observability solution.