Lyngdorf MCP Server

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 explains the tool's behavior: it increases volume in 0.5dB increments, notes that single steps are 'barely audible' due to the logarithmic nature of dB, and provides typical volume ranges for context. It doesn't mention error conditions or side effects, but covers the core operational behavior well.

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 and concise. It front-loads the core purpose, then provides volume guidance with clear, actionable recommendations. Every sentence adds value: the first states the tool's function, the second explains dB perception, the third gives step recommendations, the fourth provides typical ranges, and the fifth offers a prerequisite check. No wasted words.

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 low complexity (1 parameter, no output schema, no annotations), the description is nearly complete. It covers purpose, usage, parameter semantics, and behavioral context. The only minor gap is lack of explicit error handling or confirmation of success, but for a simple volume adjustment tool, this is sufficient.

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 meaningful context beyond the schema: it explains that steps are in 0.5dB units, clarifies that single steps are 'barely audible,' and provides practical recommendations (4-6 steps for moderate changes, 10+ for significant changes). This enhances understanding of the parameter's impact.

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 tool's purpose: 'Increase volume by 0.5dB steps.' It specifies the verb ('increase') and resource ('volume') with precision. However, it doesn't explicitly differentiate from its sibling 'setVolume' or 'volumeDown', which would be needed for a perfect score.

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 explicit guidance on when to use this tool: 'For noticeable changes: use 4-6 steps (2-3dB) for moderate adjustments, or 10+ steps for significant changes.' It includes practical recommendations for different scenarios (quiet background, moderate, loud) and advises to 'Always check current level first,' which implicitly suggests using 'getVolume' before invoking this tool.

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