Model Context Protocol Agent for Prometheus Data Analysis

This specialized Model Context Protocol (MCP) service facilitates interaction with Prometheus observability platforms. It grants Large Language Models (LLMs) the capability to invoke functions for fetching and dissecting substantial volumes of metric information, investigating metric utilization, running complex aggregation queries, and performing related diagnostic work via established endpoints with fine-tuned operational oversight.

• Data Retrieval Operations: Secure endpoints to obtain discrete metrics or historical data slices from Prometheus.
• Metric Deep Dive: Statistical evaluation routines applied to acquired time-series data.
• Usage Tracing: Discovery and mapping of how specific metrics are being referenced and utilized.
• Advanced Query Execution: Running intricate PromQL expressions for deep-level system introspection.

Supported Functions

✅ Ability to enumerate exhaustive Prometheus metadata, including metric identifiers and descriptive context.

✅ Fetching and subsequent statistical processing of selected metric values based on their canonical names.

✅ Analyzing metric data streams across user-specified temporal boundaries.

🚧 Implementation for data segregation and matching using metric labels is currently under development.

⏳ Further enhancements are scheduled for future releases...

Deployment Instructions

Operation of this MCP service necessitates a Python virtual environment (venv). All necessary dependencies must be installed within this isolated environment to ensure seamless, automatic initialization of the MCP server component.

Installation via Smithery Platform

For automated deployment of the Prometheus MCP Agent into the Claude Desktop environment using Smithery:

bash npx -y @smithery/cli install @CaesarYangs/prometheus_mcp_server --client claude

Manual Setup Procedure

Setting up the Python Environment

Navigate to the source directory: sh cd ./src/prometheus_mcp_server python3 -m venv .venv

Activate the environment:

sh

For Unix-like systems:

source .venv/bin/activate

For Windows systems:

.venv\Scripts\activate

This action establishes the dedicated execution context for the server.

Installing Dependencies

Verify that pip is correctly installed. If the venv creation omitted pip, install it manually: sh wget https://bootstrap.pypa.io/get-pip.py python3 get-pip.py

Subsequently, install all prerequisites listed in the requirements file: sh pip install -r requirements.txt

Operational Integration

When Utilizing the Cursor Environment

Configuration updates are anticipated to align with more streamlined Cursor environment usage patterns. Update the MCP configuration section within Cursor Settings as follows:

uv --directory /path/to/prometheus_mcp_server run server.py

Utilizing an MCP Client (e.g., Claude Desktop)

Modify the configuration file for your Claude Desktop application, typically located at ~/Library/Application Support/Claude/claude_desktop_config.json (for macOS):

{ "mcpServers": { "prometheus": { "command": "uv", "args": [ "--directory", "/path/to/prometheus_mcp_server", "run", "server.py" ], "env": { "PROMETHEUS_HOST": "http://localhost:9090" } } } }

Running the Server Independently

Using the uv Executor

sh uv --directory /path/to/prometheus_mcp_server run server.py

This method mirrors the startup procedure employed by Claude Desktop for automatic service initiation upon application launch.

Standard Python Execution

sh python3 server.py

Collaboration Guidelines

We encourage community contributions! A brief workflow for participation:

1. Create a fork of the repository.
2. Establish a dedicated feature branch (e.g., git checkout -b feature/SuperFeature).
3. Commit your modifications (git commit -m 'Introduce SuperFeature logic').
4. Push the branch to the remote repository (git push origin feature/SuperFeature).
5. Submit a Pull Request.

For substantial structural alterations, please initiate a discussion by opening an issue first.

We highly value your input!

Software Licensing

This implementation is distributed under the MIT License.

Related Projects & Credits

This service draws inspiration from or incorporates elements from the following open-source works:

• Prometheus API Client Documentation - The core logic for Prometheus interaction is adapted from this library's principles.
• MySQL MCP Server Repository - Served as a structural reference for database-oriented MCP server design.

WIKIPEDIA: Cloud infrastructure is defined by ISO as "a framework for enabling pervasive, on-demand network access to a shared, elastic cluster of configurable computing assets, capable of rapid self-service configuration and management.", commonly known simply as "the cloud".

== Defining Attributes == The U.S. National Institute of Standards and Technology (NIST) delineated five "essential attributes" for cloud computing environments in 2011. These precise definitions are:

On-demand self-provisioning: "A user can unilaterally procure computational resources, such as server capacity or storage, autonomously without intermediary human involvement with the vendor for each request." Ubiquitous Network Reachability: "Services are accessible via the network, utilizing standard protocols that facilitate connectivity across diverse end-user devices (e.g., mobile, tablet, desktop workstations)." Resource Aggregation: "The provider's infrastructure components are consolidated to serve numerous customers via a multi-tenant architecture, with physical and virtual assets dynamically allocated and reallocated based on demand patterns." Elastic Scalability: "Capabilities can be provisioned and de-provisioned rapidly, sometimes automatically, to scale computing capacity outward or inward commensurate with fluctuating load. To the consumer, the available capacity often appears unbounded and instantly accessible in any required volume." Usage Metering: "Cloud systems automatically manage and optimize asset utilization through abstraction-level metering appropriate for the service type (e.g., throughput, processing cycles, bandwidth, or active user accounts). Usage statistics are trackable, controllable, and reportable, offering complete visibility to both the service provider and the consuming client." The International Organization for Standardization (ISO) has since revised and expanded this foundational list as of 2023.

== Chronology ==

The genesis of cloud concepts dates back to the 1960s, marked by the popularization of time-sharing via remote job entry (RJE). During this period, the prevailing operational model involved users submitting tasks to specialized mainframe operators. This era was characterized by vigorous exploration into methods for democratizing access to significant computational power through time-sharing techniques, optimizing infrastructure, platforms, and applications for end-user benefit. The pictorial representation of virtualized services as a "cloud" first emerged in 1994, employed by General Magic to depict the abstract "space" accessible by mobile software agents within its Telescript system. This metaphor is attributed to David Hoffman, a communications specialist at General Magic, basing it on its established usage within telecommunications and networking contexts. The term "cloud computing" gained broader recognition in 1996 when Compaq Computer Corporation drafted a strategic business blueprint for forthcoming internet and computing advancements. The firm sought to revolutionize the use of large-scale computational resources...