TOON MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool analyzes data and shows potential token savings, but doesn't describe how this is done (e.g., computational method, output format, performance implications). It also omits details like whether it's read-only, if it has side effects, rate limits, or error handling. For a tool with no annotations, this is a significant gap in transparency.

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 that directly states the tool's function without unnecessary words. It's front-loaded with the core action ('Analyze data') and outcome ('show potential token savings'), making it easy to parse. Every part of the sentence contributes to understanding the tool's purpose.

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 complexity of analyzing data for token savings and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'TOON encoding' is, how token savings are calculated, or what the output looks like (e.g., a report, numerical value, comparison). For a tool with no structured output and behavioral gaps, more context is needed to guide effective use.

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, with clear documentation for both parameters ('data' and 'format'), including an enum for 'format'. The description doesn't add any meaningful semantic details beyond what the schema provides (e.g., it doesn't explain what 'token savings' means in context or how 'format' affects analysis). Given the high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.

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: 'Analyze data and show potential token savings with TOON encoding.' It specifies the verb ('analyze'), resource ('data'), and outcome ('show potential token savings'), which is specific and actionable. However, it doesn't explicitly differentiate this tool from its siblings (e.g., toon_encode, toon_decode, toon_optimize_prompt), which would be needed for a score of 5.

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 no guidance on when to use this tool versus its siblings. It doesn't mention alternatives, prerequisites, or contextual cues for selection. For example, it doesn't clarify if this should be used before encoding, after decoding, or in comparison to optimization. This lack of usage context leaves the agent without direction.

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