cavalry_connect

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

No annotations are present, so the description must disclose behavioral traits. It mentions connecting and the `force` parameter but does not state whether connections are destructive, whether permissions are needed, or what happens to existing connections. This is insufficient for a mutation tool.

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?

Two sentences efficiently convey the purpose and examples with no unnecessary words. The information is front-loaded.

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?

With 5 parameters, no output schema, and no annotations, the description covers the basic purpose but omits details like return value, side effects, or prerequisites. It is adequate but not comprehensive.

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 100% description coverage, so the baseline is 3. The description adds value by providing concrete examples for `fromAttrId` and `toAttrId` ('id' and 'shapes'), giving semantic context beyond 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 action ('Connect one layer's attribute output to another layer's attribute input') and provides specific examples (shape to duplicator, noise to position), distinguishing it from sibling tools like cavalry_set_attribute or cavalry_parent.

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 explicitly says it's for procedural/data-driven workflows and gives concrete examples, but does not mention when not to use it or what alternatives exist (e.g., cavalry_inspect_connections for inspecting connections).

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