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. It effectively adds context beyond basic purpose: it mentions pagination ('Results are paginated'), which is crucial for understanding how results are returned, and it warns about misuse for archiving, hinting at performance or data management considerations. However, it does not cover other behavioral traits like rate limits, authentication needs, or error handling, leaving some gaps.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by key behavioral notes (pagination) and usage guidelines. Every sentence adds value without redundancy, and the structure is clear with bullet-like separation. There is no wasted text, making it efficient for an AI agent to parse.

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 (a search tool with no parameters but pagination and archiving warnings), no annotations, and no output schema, the description is moderately complete. It covers purpose, pagination, and a key usage warning, but lacks details on output format, error conditions, or authentication requirements. For a tool with no structured support, it should do more to compensate, but it meets a minimum viable level.

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 no parameters are documented in the schema. The description does not mention any parameters, which is appropriate here since there are none. It adds value by implying the tool uses a 'log search query' for filtering, but without parameters, the baseline is high. A score of 4 reflects that the description compensates well for the lack of parameters by explaining the query-based nature.

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 ('match a log search query'), making the function evident. However, it does not explicitly differentiate from sibling tools like 'logs_aggregate_analytics' or 'search_events', which reduces clarity slightly.

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: 'Use this endpoint to build complex logs filtering and search' and advises against using it for archiving, pointing to Datadog archive capabilities instead. This gives implied guidance on when to use it (complex filtering/search) and when not to (archiving), but it lacks explicit alternatives or comparisons with sibling tools, such as 'logs_aggregate_analytics' for analytics vs. listing.

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