Fetch Datadog metric timeseries points for a specified time range and query to inspect and analyze metric trends.
Query timeseries points of metrics from Datadog
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | End of the queried time period, seconds since the Unix epoch. | |
| from | Yes | Start of the queried time period, seconds since the Unix epoch. | |
| query | Yes | Datadog metrics query string. e.g. "avg:system.cpu.user{*} |
Changes observed during successful MCP inspections.
v1.8.0Does 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. It states the tool queries metrics but doesn't mention any behavioral traits like rate limits, authentication needs, error handling, or what the query returns (e.g., data format, pagination). This leaves significant gaps for a tool that interacts with an external service like Datadog.
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 a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to understand at a glance.
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 complexity of querying an external metrics service (Datadog), no annotations, and no output schema, the description is incomplete. It lacks details on return values, error cases, or any contextual information needed for effective use, making it inadequate for a tool with three parameters and potential behavioral nuances.
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 input schema has 100% description coverage, clearly documenting all three parameters (from, to, query) with examples. The description adds no additional meaning beyond what the schema provides, such as explaining query syntax further or usage nuances, so it meets the baseline for high schema coverage.
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 ('Query') and resource ('timeseries points of metrics from Datadog'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'list_metrics' or 'get_monitors', which might have overlapping functionality, so it misses full sibling differentiation.
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 provides no guidance on when to use this tool versus alternatives such as 'list_metrics' or 'get_monitors', nor does it mention any context or exclusions for usage. It's a basic statement of function without operational context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.