x402-ai-cite
AI Cite: Generate citations with AI.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| style | No | Style to process | |
| content | No | Content to process |
AI Cite: Generate citations with AI.
| Name | Required | Description | Default |
|---|---|---|---|
| style | No | Style to process | |
| content | No | Content to process |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden of behavioral disclosure. It does not mention output format, limitations, cost, determinism, or side effects, leaving the agent with only a vague generative promise. It is not misleading, but it provides minimal behavioral context.
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 extremely short and front-loaded, but it contains redundant filler ('AI Cite:', 'with AI') and omits substantive details. It is concise rather than complete, earning only a mid-range score for structure.
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?
With no output schema and no annotations, the agent needs at least an explanation of the expected return value and what constitutes valid inputs. The description does not provide any of this, so it is insufficient for reliable tool invocation despite the low parameter count.
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 description coverage is 100%, which sets baseline 3. However, the tool description itself adds nothing about what 'style' or 'content' should contain; the schema's 'Style to process' and 'Content to process' are generic and uninformative without examples or allowed values.
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 states a specific verb and resource: generate citations. However, the phrase 'with AI' is generic and does not differentiate this from the many other x402-ai-* siblings, though no other sibling is explicitly a citation generator.
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 use this tool versus alternatives such as other x402-ai-* tools or citation-related utilities. The description does not mention preferred use cases, edge cases, or exclusions.
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.