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minia2a-mcp

x402-mastermind

Mastermind: Bulls/cows guessing game logic.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
opNoOp to process
codeNoCode to process
guessNoGuess to process
actionNoAction to process
digitsNoDigits to process
secretNoSecret to process

TDQS

C2.6/5.0
Behavior2/5

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

With no annotations present, the desccription carries the full burden of disclosure, but it only hints at bulls/cows game logic. It doesn't state the output format, whether a secret code is required or generated, what inputs are necessary, or how validation/errors behave. For a game-logic tool, the core I/O contract is missing.

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

Conciseness4/5

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

The description is a single, short phrase with no wasted words; the game name and scoring method are front-loaded. It is efficient but somewhat fragmentary, lacking a verb, which makes it less strructured than a complete sentence description.

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?

With six undocumented meaningful parameters, no output schema, and no annotations, this description is far too sparse. An agent cannot determine what to pass, which values op or action should take, or what the return value looks like. It needs at least a sentence covering inputs, operation, and expected output.

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

Parameters3/5

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

All six parameters have schema descriptions ('Op to process', 'Code to process', etc.), but they are generic placeholders that add no real meaning. The tool description provides some context by implying code, guess, and secret relate to Mastermind, but it leaves op, action, and digits unexplained. Nominal coverage is 100%, so baseline 3 applies, but the description compensates only minimally.

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 names the game (Mastermind) and the scoring method (bulls/cows), which tells an agent this is a code-guessing logic tool. However, it lacks a verb or explicit operation — it doesn't say whether it scores a guess, generates a secret, or processes a move. It is distinguishable from Wordle-style tools but still ambiguous about the actual action.

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 usage guidance is provided. The description does not say when to use this tool versus related game-logic tools like x402-wordle-solve, x402-guess-number, or other x402-* game tools, and it doesn't mention preconditions or exclusions. An agent must infer intent from the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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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.

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