llmconveyors-mcp

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

No annotations provided, so description carries full disclosure burden. Covers mutation ('Modifies the user's consent record'), authorization ('Requires scope: settings:write'), and side effects with specific enumeration example. No gaps for a destructive consent operation.

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?

Four sentences precisely structured: purpose → consequences → auth/modification → prerequisites. Every sentence earns its place. No redundancy with schema data. Front-loaded with action verb.

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 single-parameter mutation tool without output schema, description comprehensively covers behavior, auth prerequisites, side effects, and workflow integration. Complete for complexity level.

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 has 100% coverage with well-documented enum. Description adds value by illustrating semantic impact of specific enum values (ai-generation affecting agent runs), elevating it above baseline 3, though it doesn't elaborate on all 6 enum options.

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?

States specific verb 'Revoke' with resource 'consent' and scope 'data processing purpose'. Distinguishes from sibling 'privacy-grant-consent' by being the inverse operation and explicitly references 'privacy-list-consents' for status checking.

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?

Provides explicit prerequisite workflow ('Use privacy-list-consents to check current status first') and warns about consequences ('may disable platform features'). Concrete example ('revoking ai-generation prevents agent runs') clarifies when-not-to-use.

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