MCP Datadog Server

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 but offers minimal information. It mentions querying and processing timeseries data but doesn't describe whether this is a read-only operation, what permissions might be required, whether it's resource-intensive, what the typical response format looks like, or any rate limits. For a tool with zero annotation coverage, this represents significant gaps in behavioral transparency.

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, clear sentence that efficiently communicates the core functionality. It's appropriately sized for a tool with no parameters, though it could be slightly more specific about what 'various data sources' includes. There's no wasted verbiage or unnecessary elaboration.

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 timeseries querying with data processing, the description is insufficiently complete. With no annotations, no output schema, and a vague description, the agent lacks critical information about what this tool actually returns, how to interpret results, what data sources are supported, or what formulas/functions are available. For a potentially complex query tool, this represents significant contextual gaps.

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 0 parameters with 100% description coverage, meaning there are no parameters to document. The description doesn't need to compensate for missing parameter documentation. The baseline for 0 parameters is 4, as the description appropriately doesn't waste space on non-existent parameters.

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 states the tool 'queries timeseries data across various data sources and processes the data by applying formulas and functions', which provides a general purpose but lacks specificity. It mentions 'various data sources' without naming them, and 'formulas and functions' without examples, making it somewhat vague. It distinguishes from many siblings by focusing on timeseries querying, but doesn't clearly differentiate from similar tools like 'query_scalars' or 'metrics_query_timeseries'.

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?

No explicit guidance on when to use this tool versus alternatives is provided. The description doesn't mention prerequisites, constraints, or when-not-to-use scenarios. Given the many sibling tools (including other query tools like 'query_scalars' and 'metrics_query_timeseries'), the absence of comparative guidance leaves the agent without clear selection criteria.

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