Skip to main content
Glama

minia2a-mcp

x402-prompt-templates

x402-prompt-templates: AI prompt template library. 🆓 5 free trial calls per registered wallet

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNoN to process
limitNoLimit to process
categoryNoCategory to process

TDQS

C2.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations at all, the description carries full responsibility for behavioral disclosure. It mentions the 5 free trial calls per registered wallet, which is useful, but does not state whether the tool lists templates, retrieves content, requires payment, or has rate limits beyond the trial. A prompt template library that charges money clearly has behavioral implications (payment/auth) that are left undisclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and the trial-calls note is front-loaded, but the emergency emoji and incomplete sentence structure (colon then standalone text) look unpolished. The brevity is fine, but it spends characters on marketing rather than functional specification.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a commercial tool with no annotations, no output schema, and three vague parameters, the description is seriously incomplete. An agent does not know what input is expected, what response shape to expect, or whether invoking it triggers a paid call. The free-trial note helps at a high level but cannot compensate for missing operational details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% in the sense every parameter has a description, but those descriptions are placeholders like 'N to process', 'Limit to process', and 'Category to process' — they reveal nothing about what N, limit, or category mean in the context of prompt templates. The description adds no parameter meaning, so an agent cannot know what values to pass.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description says 'AI prompt template library' with a specific resource and domain, so an agent can infer it provides reusable prompt templates. However, it lacks a distinguishing verb or details on what operations are supported (list, retrieve, generate?), and with thousands of sibling tools named similarly, an agent cannot tell what makes this distinct beyond the generic library label.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like x402-ai-prompt-optimize, x402-premium-ai-toolkit, or other prompt-related tools. The description only mentions a free trial allowance, which is a commercial constraint, not a usage condition. An agent must guess when this library 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.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

D1.6/5.0
Disambiguation1/5

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.

Naming Consistency2/5

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.

Tool Count1/5

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.

Completeness2/5

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.

Resources