Hardened Google Workspace MCP

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions that gradient_points 'creates a gradient rule' and overrides other parameters, which is useful behavioral context. However, it doesn't disclose important traits like required permissions, whether this is a destructive/mutating operation, rate limits, or error conditions for a tool with 9 parameters.

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 well-structured with clear sections (Args, Returns) and front-loaded purpose statement. Most sentences earn their place by explaining parameter behaviors, though some explanations could be more concise. The gradient_points explanation is particularly dense but necessary.

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 complex 9-parameter mutation tool with no annotations, the description provides good parameter semantics and output clarification. However, it lacks important contextual information about authentication requirements, error handling, and the tool's mutating nature. The presence of an output schema helps with return values, but behavioral context remains incomplete.

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?

With 0% schema description coverage, the description provides substantial parameter semantics beyond the schema. It explains each parameter's purpose, gives examples for condition_type, clarifies data formats for condition_values and gradient_points, and describes interactions between parameters (gradient_points overriding others). This compensates well for the schema's lack of descriptions.

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 specific action ('Adds a conditional formatting rule') and target resource ('to a range'), distinguishing it from sibling tools like 'delete_conditional_formatting' and 'update_conditional_formatting' that handle different operations on the same resource type.

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 usage context through parameter explanations (e.g., 'If provided, a gradient rule is created and boolean parameters are ignored'), but doesn't explicitly state when to use this tool versus alternatives like 'format_sheet_range' or 'update_conditional_formatting'. No explicit when-not-to-use guidance is provided.

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