send_custom_metric

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

Annotations already indicate a write operation (readOnlyHint=false) with open-world side effects. The description adds value beyond annotations by explaining the semantics of gauge, count, and rate metric types, which are not detailed in the schema. It does not discuss data persistence or rate limits, but the annotations cover the safety profile adequately, so the description adds useful context without contradicting structured data.

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 compact and well-structured: a one-line summary, a 'Use this when' sentence, and a bulleted list of metric types. Every sentence contributes to understanding the tool's purpose and usage. There is no redundancy or filler, and the most important information is front-loaded.

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 complexity (6 parameters, nested points array) and the rich schema and annotations, the description is mostly complete. It covers the core use case and metric type semantics, and the schema already documents parameter formats. The only minor gap is that it does not mention any prerequisites (e.g., authentication) or explicitly contrast with read-only metric tools, but these are partially covered by annotations and sibling context.

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 description coverage is 100%, so the baseline is 3. The description adds meaningful semantic detail for the metric_type parameter by defining each allowed value (gauge, count, rate) with real-world examples (e.g., temperature for gauge, events per second for rate). This enhances understanding beyond the schema's brief description, so it earns a 4.

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 starts with a specific verb and resource: 'Send custom metric data points to Datadog.' This clearly states what the tool does and distinguishes it from sibling tools like query_metrics or list_available_metrics, which are read-oriented. The 'Use this when' clause further clarifies its intended use for tracking custom application metrics or business KPIs.

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 states when to use the tool: 'Use this when: need to track custom application metrics or business KPIs.' It also explains the three metric types with examples, helping the agent choose the correct type. However, it does not explicitly mention alternatives like query_metrics for reading metrics, so it lacks an explicit 'when not to use' clause.

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