x402-ngram
Ngram: Generate n-grams from an array of values. Provide values and optional n (default 2); returns consecutive n-item groups.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Ngram: Generate n-grams from an array of values. Provide values and optional n (default 2); returns consecutive n-item groups.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and does add some behavioral detail: n defaults to 2 and the result is composed of 'consecutive n-item groups'. It does not disclose how invalid inputs are handled, whether windows overlap, or what happens when the array is shorter than n, but those are relatively minor for a pure utility.
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?
Two short sentences deliver the core purpose, input semantics, default behavior, and return shape. There is no wasted text and the essential information is front-loaded.
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 output schema and no annotations, so the description must be self-sufficient for correct invocation. While it explains the concept, the formal input schema lists zero parameters, which conflicts with 'Provide values and optional n.' An agent cannot reliably know how to pass these inputs, and no return example or edge-case behavior is provided.
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, so the description is the only source of parameter meaning. It names both values and n, states that n is optional, gives its default, and clarifies values should be an array. This is solid compensation for the schema omission, though it does not specify the exact expected type/format of n or the array elements.
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 a specific operation: 'Generate n-grams from an array of values' with a concrete output ('consecutive n-item groups'). It is not a tautology and an agent can tell what the core action is. However, it does not differentiate from the closely named sibling x402-ngram-gen or the overlapping x402-bigrams.
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?
No guidance is given about when to prefer this tool over x402-ngram-gen, x402-bigrams, or other n-gram-related siblings. The description only explains the procedure, not the selection context or edge cases that would route an agent here.
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.