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prometheus_query

Execute PromQL queries to retrieve and analyze cluster metrics from Prometheus, supporting instant and range queries with automatic endpoint discovery.

Instructions

Execute PromQL queries against Prometheus for cluster metrics.

Supports instant and range queries with automatic endpoint discovery and authentication.

Args:
    query: PromQL query string.
    query_type: "instant" or "range" (default: "instant").
    start_time: Start for range queries (ISO 8601 or Unix timestamp).
    end_time: End for range queries (ISO 8601 or Unix timestamp).
    step: Step interval for range queries (default: "300s").
    cluster: Cluster domain override.
    format: "json", "table", or "csv" (default: "json").
    namespace_filter: Regex to filter by namespace.
    limit: Max results to return.
    timeout: Query timeout in seconds (default: 30).

Returns:
    Dict: Query results, metadata, execution info, and analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
query_typeNoinstant
start_timeNo
end_timeNo
stepNo300s
clusterNo
formatNojson
namespace_filterNo
limitNo
timeoutNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: 'automatic endpoint discovery and authentication,' which are not inferable from the schema. However, it does not mention rate limits, error handling, or side effects, leaving some gaps in behavioral context.

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

Conciseness4/5

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

The description is well-structured with a clear purpose statement, behavioral note, and organized parameter list. It is appropriately sized but could be slightly more concise by integrating the 'Returns' section more seamlessly, though it remains efficient and front-loaded.

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

Completeness5/5

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

Given the tool's complexity (10 parameters, no annotations), the description is complete: it explains purpose, behavior, all parameters with semantics, and mentions return values. With an output schema present, it need not detail return structure, making it adequately comprehensive.

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

Parameters5/5

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

The schema description coverage is 0%, so the description must compensate fully. It provides detailed semantics for all 10 parameters, including defaults, formats (e.g., ISO 8601), and options (e.g., 'instant' or 'range'), adding significant value beyond the bare schema.

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: 'Execute PromQL queries against Prometheus for cluster metrics.' It specifies the verb ('Execute'), resource ('PromQL queries'), and target ('Prometheus for cluster metrics'), making it distinct from sibling tools that focus on logs, events, pipelines, or other resources.

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

Usage Guidelines3/5

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

The description implies usage by mentioning 'Supports instant and range queries,' but does not explicitly state when to use this tool versus alternatives like 'analyze_logs' or 'detect_anomalies.' It provides context for query types but lacks explicit guidance on tool selection among siblings.

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

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