Get Metric Data

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

Annotations already indicate readOnlyHint=true and idempotentHint=true, so the tool is clearly non-destructive. The description adds important behavioral context: it retrieves data, explains custom metric storage per-node, and requires specific paths and rollup=false for those. It also describes the return format (array of metric data objects). 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.

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

The description is well-organized with sections, bullet points, and examples. It starts with the core purpose, then provides usage guidance, custom metric details, and a parameter list. Every sentence is informative. Despite length, it remains focused and efficient.

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 no output schema, the description explains return values (timestamps, min, max, avg, count, sum). It covers both standard and custom metrics, references the sibling discovery tool, and provides navigation patterns. For a tool with 4 parameters and good annotations, this is a complete and informative description.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by providing concrete examples for metricPath (including wildcard patterns), explaining the default duration (60 minutes), and clarifying the rollup parameter's effect on aggregation and custom metrics. These details go beyond the schema descriptions.

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 'Query any metric from the AppDynamics metric tree.' It distinguishes itself from sibling tools like appd_browse_metric_tree (which discovers paths) and appd_get_anomalies (which retrieves anomalies). The tool is presented as a generic, all-encompassing metric retrieval tool.

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 explicitly recommends using appd_browse_metric_tree first to discover metric paths. It also explains when to use rollup=true vs false, especially for custom metrics. While it doesn't list explicit alternatives or exclusions, it provides clear context for different scenarios.

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