x402-gravatar
Gravatar: Generate Gravatar URLs from email.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| size | No | Size to process | |
| No | Email to process |
Gravatar: Generate Gravatar URLs from email.
| Name | Required | Description | Default |
|---|---|---|---|
| size | No | Size to process | |
| No | Email to process |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the full behavioral burden, but it only restates the basic purpose. It does not disclose how the email is processed, whether a default size is applied, whether the URL is HTTPS, or what the output format 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 a single concise sentence with no filler. It is front-loaded and easy to parse, though its brevity contributes to the lack of behavioral and usage detail.
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 two-parameter URL generator, this is minimally adequate: an agent can infer the core operation. However, the lack of any output schema, no mention of defaults, and no clarification that email is effectively required make the description incomplete for fully correct invocation.
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 baseline is 3, but the parameter descriptions ('Size to process', 'Email to process') are tautological. The main description adds little beyond naming the email-to-URL relationship, so it does not improve parameter understanding.
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 identifies the action ('Generate Gravatar URLs') and the required input ('from email'). However, it does not differentiate this tool from the nearly identical sibling x402-gravatar-url, so it stops short of full sibling distinction.
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 usage guidance is provided: there is no mention of when to choose this tool over x402-gravatar-url, x402-avatar-generate, or x402-initials-avatar. The description gives no exclusions, prerequisites, or alternative selection criteria.
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.