x402-text-entropy
Text Entropy: Text Entropy
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Text Entropy: Text Entropy
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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, and it discloses nothing. It does not state whether the tool reads text, computes a score, returns a value, has side effects, or anything else about its behavior.
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 short, but this is under-specification rather than conciseness. Four words that repeat the tool name earn no credit for brevity because they contain no information an agent can act on.
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 no annotations, no output schema, and an empty parameter schema, so the description is the only source of guidance — and it provides none. The description is completely inadequate for an agent to understand what this tool does, what it returns, or when to invoke it.
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 is empty (0 parameters), and the rules set a baseline of 4 when there are no parameters to document. The description adds no parameter meaning, but there is also nothing for it to explain; the main risk is that the agent cannot tell what input the tool operates on, which is a purpose problem rather than a parameter-semantics problem.
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 'Text Entropy: Text Entropy' is a pure tautology that merely restates the tool name. It names no verb, no resource, and no behavior, so an agent cannot tell what the tool computes. It is also indistinguishable from siblings like x402-entropy, x402-shannon-entropy, and x402-password-entropy.
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 zero guidance on when to use this tool or how it differs from the several entropy-related siblings (x402-entropy, x402-shannon-entropy, x402-password-entropy, x402-char-diversity). An agent has no basis to select this tool over its alternatives.
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.