x402-ai-email-draft
AI Email Draft: Draft an email with AI.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tone | No | Tone to process | |
| topic | No | Topic to process | |
| context | No | Context to process |
AI Email Draft: Draft an email with AI.
| Name | Required | Description | Default |
|---|---|---|---|
| tone | No | Tone to process | |
| topic | No | Topic to process | |
| context | No | Context to process |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of explaining behavior, but it only says that it drafts an email with AI. It does not disclose what happens with the tone, topic, or context inputs, what form the output takes, or any constraints. For a generative tool, this is insufficient behavioral detail.
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 and front-loaded, with no verbose filler. However, it wastes words repeating the tool name concept ('A Email Draft') and says 'with A' redundantly. It is concise in length but borders on under-specification rather than being an efficient, information-dense definition.
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 that there are no annotations, no output schema, and a direct sibling tool with a nearly identical purpose, the description is not complete enough for confident selection and invocation. It does not explain how the three optional parameters shape the email, what the output structure looks like, or why this should be chosen over x402ai-email-writer.
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%, so the schema already documents all three parameters. The tool description adds no additional parameter meaning, but the baseline of 3 applies because the schema covers them. The schema descriptions themselves are generic ('Tone to process'), so the description could have added value but does not.
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 action and resource: 'Draft an email with AI.' This gives an agent a basic understanding of what the tool does. However, it does not distinguish this from the very similar sibling tool x402-ai-email-writer, so it lacks sibling differentiation.
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 versus alternatives such as x402ai-email-writer or x402ai-cold-outreach. The description gives no context about scenarios, prerequisites, or exclusions, leaving the agent to guess when this is the right choice.
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.