Optimize JSON or YAML content to reduce token consumption using TOON format for efficient processing.
Optimize structured data content for token efficiency using TOON format
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | The content to optimize (JSON or YAML) | |
| toolName | No | Name of the tool that generated this content |
Changes observed during successful MCP inspections.
v0.8.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It states 'optimize' but does not specify whether the input is modified in place, whether the operation is reversible, what the output format is, or any side effects. The TOON format is mentioned but never explained, leaving the agent to guess about the transformation's nature.
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 a single, efficient sentence with no fluff. It is appropriately front-loaded with the core action and format. However, it is so brief that it sacrifices necessary detail, though that is more a completeness issue than a conciseness issue.
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?
The tool has only two parameters and no output schema, so the description should compensate by explaining what the output looks like, what TOON format entails, and any prerequisites. None of this is provided. An agent cannot predict the result of calling this tool beyond a vague notion of optimization, making the description incomplete for safe invocation.
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 coverage is 100%, so both parameters are already documented. The description adds no additional meaning about 'content' or 'toolName'—for instance, why toolName might be needed or how it influences optimization. Since the schema already covers the basics, a baseline of 3 is appropriate, but there is room for added context that the description does not provide.
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 uses a specific verb ('Optimize') and resource ('structured data content') and introduces the TOON format, which clearly distinguishes it from sibling tools focused on caching and stats. The purpose is unambiguous and does not rely on the tool name.
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?
There is no guidance on when to use this tool versus alternatives, nor any mention of conditions or exclusions. The description merely states what it does without helping the agent decide when to invoke it, even though the sibling tools are clearly different in function.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.