Skip to main content
Glama
mshegolev

mshegolev/prometheus-mcp

prometheus_health_check

Read-onlyIdempotent

Check if Prometheus is live and ready by testing its health and readiness endpoints. Use this to troubleshoot empty query results by confirming Prometheus is running.

Instructions

Check Prometheus liveness and readiness.

Calls GET /-/healthy and GET /-/ready — management endpoints outside the /api/v1 namespace. Returns whether each probe returned a 200 status code.

Use this to verify Prometheus is actually running before investigating blank query results. A failed health check means Prometheus is down; a failed readiness check means it's starting up or shutting down.

Examples: - Use when: "Why are all my queries returning empty results?" → check if Prometheus is healthy first. - Use when: Setting up a new MCP connection — verify the target is reachable and healthy. - Don't use when: You want metric values (call prometheus_query).

Returns: dict with healthy (bool), healthy_status_code, ready (bool), ready_status_code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instanceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
readyYes
healthyYes
ready_status_codeYes
healthy_status_codeYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral context by detailing the specific endpoints, that they are outside /api/v1, and that it returns boolean statuses plus status codes. There is no contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-organized with sections for endpoints, return format, usage examples, and a clear 'Don't use' guideline. It is concise, with no superfluous text, and each sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the description covers usage, behavior, and return values, it misses documenting the optional 'instance' parameter. Given the tool's simplicity (one parameter, existing output schema, and rich annotations), the description is mostly complete but has a notable gap in parameter semantics.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one optional parameter 'instance' with 0% schema description coverage, but the description does not explain what 'instance' does or how to use it. For a tool with a single parameter, this omission forces the agent to guess, which could lead to incorrect invocations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Check Prometheus liveness and readiness.' It specifies the endpoints called (GET /-/healthy and GET /-/ready) and the action (returns whether each probe returned a 200). This distinguishes it from sibling tools like prometheus_query, which retrieves metric values.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance, including when to use ('verify Prometheus is actually running before investigating blank query results') and when not to use ('Don't use when: You want metric values (call prometheus_query)'). It includes concrete examples, making it clear for an AI agent when to invoke this tool over alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mshegolev/prometheus-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server