OMENZ agent tools

terms_extract

API Terms Extractor / Data-License Checker: before your agent uses a dataset or API commercially: a terms/license page -> clauses by category (commercial use, attribution, rate limits, retention, training on your data, prohibited uses, resale, refunds, liability, termination, license type) with quotes, plus plain flags (no_commercial_use, attribution_required, may_train_on_your_data, no_resale, no_scraping, non_refundable). Pattern-based, not legal advice. By OMENZ. (MCP calls run in AYNE, the free playzone; production: GET https://www.omenz.org/api/tollbooth/v1/terms-extract, $0.005 via x402.)

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": {
      "url": "<url>"
    },
    "name": "terms_extract"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 14, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does meaningful work: it discloses the method ('pattern-based, not legal advice'), the output shape, and cost/access ($0.005 via x402, alternate production endpoint). It omits operational behavior an agent would want: whether it fetches and renders the page (JS, PDFs, paywalls), failure modes, and latency.

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 clause and flag enumeration is front-loaded and genuinely useful, but the sentence is a dense run-on, and '(By OMENZ)' plus the AYNE playzone/'production endpoint' sales parenthetical consume space without helping the agent decide or call the tool.

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?

For a one-parameter tool with no output schema, the description supplies what the schema cannot: the category/flag vocabulary an agent can expect back. It stops short of the fetch-behavior and error-handling details that would make it fully self-sufficient, but nothing essential for a first correct call is missing.

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 coverage is 0% for the single url parameter. The phrase 'a terms/license page' implies the URL should point at the license/terms document rather than a homepage, which adds some meaning, but format expectations (absolute URL, page vs. PDF) and whether it fetches or accepts pasted text remain undocumented.

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

Purpose5/5

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

States a specific verb and resource: it takes a terms/license page and extracts clauses into named categories (commercial use, attribution, retention, resale, etc.) plus named boolean flags. The enumerated output taxonomy makes the tool's function unmistakable and hard to confuse with siblings like terms_changed or contract_build.

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

Usage Guidelines4/5

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

Gives an explicit trigger condition: run it 'before your agent uses a dataset or API commercially.' It does not name alternatives or say when not to use it (e.g., for a diff of changed terms), so routing between this and terms_changed is left to inference.

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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