ScorpTech x402 API

Text Stats

text_stats

Read-onlyIdempotent

Text statistics: characters, words, unique words, sentences, paragraphs, reading/speaking time, Flesch readability (English), language detection (27 languages/scripts), top keywords. Price: $0.005 USDC per successful call (x402, Solana). Same tool over HTTPS: GET https://scorptech.it.com/api/x402/v1/text-stats

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "text_stats"
  }
}

Input Schema

Name	Required	Description
`top`	No	Top keywords 0-50, default 10
`wpm`	No	Reading speed 50-1000, default 238
`text`	Yes	Text to analyse (query string; keep under ~6 KB for URL limits)

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/openWorld, so the safety profile is covered. The description adds genuinely useful context beyond that: a per-call cost ($0.005 USDC via x402 on Solana) and an alternate HTTPS endpoint. It still says nothing about latency, error behavior on failed calls, or whether failed calls are charged.

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?

Dense and front-loaded: the capability list comes first, then cost and alternate endpoint. One long metric sentence plus two short operational sentences, with no filler. Slightly list-heavy but every clause carries information.

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

Completeness4/5

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

With no output schema, the description carries the burden of explaining return values and does so by enumerating the returned metrics; the input size caveat lives in the schema. What is missing is the output shape (field names/structure) and how language detection interacts with the English-only Flesch note, but an agent can call it correctly.

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?

Schema description coverage is 100% with 3 documented parameters (text, top 0-50, wpm 50-1000), so the schema already does the heavy lifting. The description adds only light qualifiers ('Flesch readability (English)', '27 languages/scripts') but does not clarify the top/wpm knobs beyond the schema — baseline 3 is appropriate.

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

Purpose4/5

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

The description enumerates the concrete metrics returned (characters, words, unique words, sentences, paragraphs, reading/speaking time, Flesch readability, language detection, top keywords), so the verb+resource is unambiguous. It does not name or contrast with the closest siblings (readability_extract, text_chunk, sentiment), which is the only thing keeping it off a 5.

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

Usage Guidelines3/5

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

Usage is only implied: an agent can infer this is the tool for whole-text metric extraction, but there is no explicit 'use this instead of readability_extract/sentiment' guidance or stated preconditions. The pricing and HTTPS endpoint note tell the agent how to call it, not when it 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.

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