list_prometheus_label_names

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions the tool's filtering capabilities, it doesn't describe important behavioral aspects like whether this is a read-only operation, what permissions are required, whether results are paginated, potential rate limits, or what format the results take. For a query tool with 5 parameters and no annotations, this leaves significant gaps in understanding how the tool behaves.

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

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

The description is perfectly concise at two sentences with zero wasted words. The first sentence establishes the core purpose, and the second sentence adds the key capability (filtering). Every word earns its place, and the information is front-loaded with the most important details first.

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

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

Given the tool's moderate complexity (5 parameters, nested filtering logic), no annotations, and no output schema, the description is minimally adequate but incomplete. It covers the basic purpose and filtering capabilities but doesn't address important contextual information like what the output looks like, error conditions, performance characteristics, or how this tool fits into broader Prometheus query workflows. The 100% schema coverage helps, but for a query tool with no output schema, more guidance on results would be beneficial.

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

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

The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning filtering by 'series selectors' (which corresponds to the 'matches' parameter) and 'time range' (which corresponds to 'startRfc3339' and 'endRfc3339'). However, it doesn't provide additional context about parameter interactions, default behaviors, or practical usage examples that would help an agent understand how to use these parameters effectively together.

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

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

The description clearly states the action ('List label names') and resource ('in a Prometheus datasource'), making the purpose immediately understandable. It distinguishes from sibling tools like 'list_prometheus_label_values' and 'list_prometheus_metric_names' by focusing specifically on label names. However, it doesn't explicitly differentiate from 'list_loki_label_names' or 'list_pyroscope_label_names' which serve similar functions for different data sources.

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

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

The description implies usage context by mentioning filtering capabilities ('Allows filtering by series selectors and time range'), suggesting when this tool might be preferred over simpler listing tools. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'list_prometheus_label_values' or 'list_prometheus_metric_names', nor does it mention any prerequisites or exclusions for usage.

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