x402-teaspoons-to-ml
Teaspoons To Ml: Convert teaspoons to ml.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Teaspoons To Ml: Convert teaspoons to ml.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 only restates the conversion operation and provides no detail about how the amount is supplied, what precision/rounding is applied, or what the response looks like.
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 very short, but the leading 'Teaspoons To Ml:' label duplicates the tool name and adds no value. The single working sentence 'Convert teaspoons to ml.' is concise and front-loaded, but some words are wasted on repetition.
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?
For a simple conversion tool this is minimal but not complete. With no input schema, no output schema, and no mention of how the quantity is passed, an agent cannot confidently know how to invoke it. Given the massive sibling list, more context about the expected call format is needed.
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 zero properties, so there are no parameters to document; the baseline of 4 applies. The description adds no parameter-level detail, but it also does not need to for an empty schema. The larger concern is the missing input mechanism, which is more of a contextual completeness issue.
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 identifies a specific verb and resource: 'Convert teaspoons to ml.' This makes the core function clear. However, it does not explicitly distinguish this from sibling tools like x402-tablespoons-to-ml or x402-units-convert beyond the name itself.
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 rather than related conversion tools such as x402-cups-to-ml or x402-units-convert. The description gives no context about expected input, output, or alternative scenarios, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tool set is saturated with near-duplicates and synonyms: character-count vs char-count, clamp vs clamp-value, is-abundant vs is-abundant-num vs is-abundant-number, and fetch vs browser-scrape vs web-scrape vs text-scrape. Generic names like 'difference', 'normalize', 'range', and 'partition' make the boundaries even harder for an agent to determine.
Most tools share a x402- kebab-case prefix, but the set mixes noun-only names (math, hash, prime, time), verb-first names (get_stats, find, validate), auto-generated names (x402-publish-1787853294312-base-account), and inconsistent variants like temp vs temperature vs temperature-convert. This is not a coherent verb_noun convention despite the common prefix.
1677 tools is an extreme count that creates selection paralysis and makes coherent agent use impractical. A utility or marketplace server at this scale needs sub-services or namespacing rather than a flat tool list.
The surface has broad token coverage across many utility categories, but the marketplace aspect is incomplete: service_discovery and get_stats exist, yet there are no generic publish, update, delete, or account-management operations. Utility families also contain redundant variants without clear completion or lifecycle structure.