aggregate

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

readOnlyHint=true already covers the key safety trait, and the description adds a useful functional trait: it can execute grouping and $lookup stages. It does not disclose potential performance implications or result-shape behavior, but with the annotation present this is acceptable.

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?

A single sentence that leads with the verb and resource, then gives concrete discriminating examples. No filler or repetition of schema content.

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?

For a read-only aggregation tool with a fully documented schema, the description plus the pipeline example is enough to select and invoke the tool. It doesn't state the default/optional nature of pipeline and returns, but those gaps are minor given the strong schema coverage.

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?

Schema description coverage is 100%, so the schema already documents collection, database, pipeline, and limit. The description only mentions pipeline stages conceptually and adds no parameter-specific meaning beyond the schema example.

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 states a specific verb ('Jalankan') and resource ('aggregation pipeline MongoDB'), and gives concrete use cases (grouping, $lookup joins) that distinguish it from simpler siblings like find/find_one. It doesn't name a sibling explicitly, but the aggregation-pipeline framing is unambiguous.

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 implies this is for data-analysis workloads requiring grouping or joins, but never states when to prefer it over find/count/distinct or when not to use it. No explicit alternatives or exclusions are provided, so the agent must infer the usage boundary.

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