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 effectively discloses key behavioral traits: the async nature ('Done async'), polling requirement ('can be repeatedly polled'), and detailed status return values (created, waiting, checked and ok, error). This covers execution mode, expected usage pattern, and outcome states, which are crucial for an agent to handle this tool correctly.

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 efficiently structured: first sentence states the purpose, second provides input reference and async nature, then bullet points detail return statuses. Every sentence earns its place with no redundancy. 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 the tool has 0 parameters, no annotations, and no output schema, the description provides good coverage: purpose, usage pattern, and detailed return statuses. It's complete enough for an agent to understand what the tool does and how to use it. The main gap is lack of explicit differentiation from sibling tools, but overall it's well-rounded for this simple tool.

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% coverage, so there's no parameter documentation burden. The description adds value by stating 'Input is the same as for `EnableAWSLogServices`', which provides semantic context about what inputs are expected (by reference) even though none are formally defined. This is helpful guidance for an agent.

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: 'Test if permissions are present to add log-forwarding triggers for the given services and AWS account.' It specifies the verb ('Test'), resource ('permissions'), and scope ('log-forwarding triggers for services and AWS account'). However, it doesn't explicitly differentiate from sibling tools like 'create_integration_aws_logs_services' or 'create_integration_aws_logs_check_asyncs', which appear related.

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: 'Input is the same as for `EnableAWSLogServices`' and 'Done async, so can be repeatedly polled in a non-blocking fashion until the async request completes.' This gives practical guidance on when to use it (for async permission checking) and how to handle it (polling). However, it doesn't explicitly state when NOT to use it or name specific alternatives among siblings.

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