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

With no annotations provided, the description carries the full burden. It discloses key behaviors: advanced tool, full column profiling, join detection, formula relationship detection, and idempotency. It does not mention permissions, side effects, or return format, but covers the primary behavioral traits well.

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 concise, with about four sentences front-loaded with the primary purpose. It is well-structured, each sentence adds value, and no wasted words. Slight deduction for not being even shorter, but overall efficient.

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 (nested objects, no output schema, many siblings), the description is fairly complete. It covers what the tool does, its outcomes, and idempotency. It could mention prerequisites or limitations, but the provided information is sufficient for most use cases.

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 input schema already documents parameters thoroughly. The description adds context about post-registration behavior (queryable, no-op) but does not provide additional parameter-level details beyond schema. Baseline 3 is appropriate.

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 explicitly states the verb 'register' and resource 'data source', and distinguishes the tool by detailing capabilities like schema profiling, formula detection, and join key detection, which sets it apart from sibling tools such as get_source_schema or connect_data.

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 mentions that re-registration is a no-op if data is unchanged, indicating idempotency and safe repeated use. It also implies that after registration, the source is queryable via query_data. However, it does not explicitly compare with alternatives or state when not to use this tool.

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