desktop-touch-mcp

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior by specifying that it lists 'active' lenses (implying a filter for non-stale items) and details the returned fields (lensId, name, target kind, etc.), which helps the agent understand the output structure. However, it lacks information on potential limitations like rate limits or error conditions.

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 front-loaded with the core purpose in the first sentence, followed by specific usage guidelines and return details. Every sentence adds value without waste, making it efficient and well-structured for quick comprehension by an AI agent.

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 simplicity (0 parameters, no annotations, no output schema), the description is largely complete. It covers purpose, usage, and output fields adequately. However, it could be slightly improved by mentioning any implicit constraints (e.g., if it only lists lenses for the current session) or error handling, but this is minor for a read-only list tool.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on usage and output. A baseline of 4 is applied since the schema fully covers the lack of parameters, and the description adds value elsewhere without redundancy.

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 verb ('List') and resource ('all active perception lenses'), making the purpose specific and unambiguous. It distinguishes this tool from siblings like perception_forget, perception_read, and perception_register by focusing on listing rather than modifying or interacting with lenses.

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 provides three use cases: 'find an existing lensId,' 'verify which windows or browser tabs are being tracked,' and 'clean up stale lenses before starting a new workflow.' This gives clear guidance on when to use this tool, helping differentiate it from alternatives like perception_read (for reading specific lenses) or perception_register (for creating new ones).

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