transformations_read

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

With no annotations provided, the description carries the full burden. 'Read operations' honestly discloses the non-mutating nature, and 'Call list_endpoints(category='transformations') for the full list' transparently admits the endpoint list can be expanded. However, it doesn't disclose response behavior, error cases, rate limits, or the relationship between the 4 endpoints and their respective path_params requirements, leaving the agent to discover these details at runtime.

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?

Three purposeful sentences, each earning its place: what the tool does, how to pass the required param, and where to find more endpoints. Front-loaded with the most important information and zero redundancy—an exemplary pattern.

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 moderately complex facade tool (4 endpoints, 4 params, no output schema), the description does well: it names the endpoints, explains the dispatch mechanism, and routes discovery to list_endpoints. The main gap is that with no output schema, the agent still has no expectation of return shapes, and the description could hint at which endpoints need which path_params—but this is honestly the best of the dimensions given the abstraction's inherent complexity.

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 baseline of 3 applies. The description adds marginal value by clarifying that `name` is a dynamic endpoint selector ('Pass the endpoint name in `name`'), which pairs well with the schema's 'Endpoint name within this resource:action group (from list_endpoints)'. But the description doesn't add semantics for body, query, or path_params beyond the schema, which already documents the {connectionId}, {groupId} placeholder pattern.

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 uses a specific verb+resource pattern ('Read operations on Fivetran transformations') and adds concrete scoping by enumerating the 4 endpoints. It's clear and specific, but it doesn't explicitly distinguish itself from sibling tools like transformation_projects_read, so the purpose is clear without being fully differentiated.

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 operational context: pass the endpoint name in `name` and call list_endpoints(category='transformations') for the full list. The cross-reference to a sibling tool enables the agent to choose correctly, and the schema note 'within this resource:action group' clarifies scope. However, it lacks explicit when-not-to-use guidance or a contrast with alternatives like transformation_projects_read.

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