MCP Datadog Server

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

The description adds significant behavioral context beyond what annotations provide. Annotations only indicate the schema structure (ZodObject with passthrough), but the description explains the async polling behavior ('Subsequent requests will always repeat the above'), the four possible status return values ('created', 'waiting', 'checked and ok', 'error'), and that it checks for Lambda existence. This provides crucial operational details for an agent.

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 appropriately sized. The first sentence states the purpose clearly, followed by important behavioral details about input equivalence and polling, then bullet points for return statuses. Every sentence adds value, and the information is front-loaded with the core purpose. It could be slightly more concise by combining some sentences, but it's efficient overall.

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 complexity (async permission checking with multiple status outcomes), no output schema, and minimal annotations, the description does a good job of providing necessary context. It explains the purpose, input relationship, polling behavior, and possible return statuses. The main gap is lack of explicit differentiation from sibling tools, but otherwise it's quite complete for a zero-parameter async checking 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 the schema fully documents the empty parameter set. The description adds value by explaining that 'The input is the same as for Enable an AWS service log collection,' which provides semantic context about what inputs would be expected if this tool had parameters. This compensates for the lack of parameter documentation in the schema.

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 a log-forwarding triggers for the given services and AWS account.' It specifies the verb ('Test'), resource ('permissions'), and scope ('log-forwarding triggers for given services and AWS account'). However, it doesn't explicitly differentiate from sibling tools like 'create_integration_aws_logs' or 'create_integration_aws_logs_services', which appear related but have different purposes.

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 some usage context by stating 'The input is the same as for Enable an AWS service log collection' and 'this endpoint can be polled intermittently instead of blocking.' This implies when to use it (for async permission checking) but doesn't explicitly state when NOT to use it or name specific alternatives among the many sibling tools. The guidance is helpful but not comprehensive.

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