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. It discloses key behavioral traits: pagination ('Results are paginated'), that it's a read operation ('List endpoint'), and a limitation ('latest logs'). It also mentions an alternative for archiving. However, it doesn't cover potential rate limits, authentication needs, or error conditions, which would be helpful for a read tool with no 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-structured and front-loaded: the first sentence states the core purpose, followed by pagination info, usage guidance, and an alternative. Every sentence adds value without redundancy. The markdown links are concise and relevant. It's appropriately sized for the tool's complexity.

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 annotations, no output schema, and 0 parameters, the description does a good job covering purpose, usage, and key behavior (pagination, latest logs, archiving alternative). However, for a log listing tool, it could benefit from more detail on output format (e.g., structure of returned logs) or error handling, which would make it more complete for agent use.

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, so the baseline is high. The description adds value by implying the tool accepts a 'log search query' (though not explicitly as a parameter) and mentions pagination behavior. Since there are no parameters to document, this is strong, but not perfect as it could clarify the query mechanism more explicitly.

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 tool's purpose: 'List endpoint returns logs that match a log search query.' It specifies the verb ('List'), resource ('logs'), and scope ('that match a log search query'). However, it doesn't explicitly differentiate from sibling tools like 'search_logs_events' or 'logs_aggregate_analytics', which prevents a perfect score.

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 clear usage context: 'Use this endpoint to see your latest logs' and offers an alternative for archiving ('consider use of the Datadog archive capabilities instead'). It also mentions pagination. However, it doesn't explicitly state when NOT to use this tool versus similar siblings (e.g., 'search_logs_events'), which would be needed for a score of 5.

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