MCP Datadog Server

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

No annotations are provided, so the description carries the full burden. It mentions the tool returns a list of services and their IDs, which implies a read-only operation, but doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or response format. For a tool with zero annotation coverage, this is a significant gap in 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 two sentences, front-loaded with the core purpose followed by usage guidance. Every word earns its place, with no wasted text. It's efficiently structured and appropriately sized for a simple, parameterless tool.

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 simplicity (0 parameters, no output schema, no annotations), the description is reasonably complete for its purpose. However, it lacks details on response format, error cases, or integration context that would help an agent use it effectively. Without annotations or output schema, the description should ideally cover more behavioral aspects to be fully complete.

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 tool has 0 parameters, with 100% schema description coverage (empty schema). The description doesn't need to add parameter semantics, as there are none to document. It appropriately focuses on the tool's purpose and usage without redundant information, earning a baseline high score for this dimension.

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: 'Get the list of current AWS services that Datadog offers automatic log collection.' It specifies the verb ('Get'), resource ('list of current AWS services'), and context ('automatic log collection'). However, it doesn't explicitly distinguish this from sibling tools like 'get_integration_aws_logs' or 'create_integration_aws_logs_services', which would be needed for 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 guidance: 'Use returned service IDs with the services parameter for the Enable an AWS service log collection API endpoint.' This indicates when to use the output, though it doesn't explicitly state when NOT to use this tool or mention alternatives among siblings. The guidance is practical but not comprehensive.

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