mcp__extractSvg

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

With no annotations provided, the description fully discloses the internal process: retrieving DSL, finding PATH nodes, resolving color references, and generating SVG markup. It also describes the return format ({ count, svgs }). However, it does not mention potential side effects or permission requirements, which would elevate transparency further.

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 concise and well-structured, starting with a clear purpose statement. Each sentence adds essential information about the tool's functionality, input methods, and output. There is no redundancy or irrelevant detail, making it easy for an AI agent to parse quickly.

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 complexity (6 parameters, conditionally required inputs) and the absence of an output schema, the description adequately captures both input options and the return structure. It covers the core behavior and output format but omits details like error handling or limitations, leaving minor gaps.

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 description coverage is 100%, so each parameter's purpose is already documented. The description adds high-level context about the workflow and output but does not enhance understanding of individual parameters beyond their schema descriptions. Thus, it delivers marginal added value, meeting the baseline for full schema coverage.

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 and resource: 'Extract SVG data from MasterGo design files.' It provides specific details about the process (retrieving DSL, finding PATH nodes, resolving color references, generating SVG markup) and distinguishes the tool's output format. This differentiates it from sibling tools like mcp__getDesignSvgs by focusing on SVG extraction specifically.

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 offers two input methods (fileId+layerId or shortLink) and notes conditionality, but it does not explicitly state when to use this tool over alternatives such as mcp__getDesignSvgs. The guidance is implied but lacks direct comparisons or exclusion criteria, leaving the agent without clear boundary conditions.

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