WritersLogic agent-infra

hedge-extract

Extract hedging and certainty cues (may, probably, clearly, approximately) from text with offsets — Finds hedge and booster cues in an agent's or source's text using a fixed English lexicon (modals, epistemic verbs, adverbs and adjectives, approximators, shield phrases, certainty boosters) plus caller-supplied phrases, and returns each cue with its category, the sentence it sits in, and offsets in UTF-16, code points, UTF-8 bytes and graphemes. Rule-based and English only: a cue is a word match,

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": "hedge-extract"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	Text to scan.
`extraCues`	No	Caller cues added to the lexicon.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 15, 2026

TDQS

A3.8/5.0

Behavior4/5

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

With no annotations at all, the description carries the full burden and does substantial work: it discloses the fixed English lexicon, the cue categories, caller-supplied phrase extension, and the four offset encodings returned. It stops short of stating match semantics for multiword phrases or how duplicate/pre-existing cues are handled, and the text is cut off mid-sentence, so the disclosure is strong but incomplete.

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 purpose is front-loaded and the density is mostly earned, but the final clause trails off ('a cue is a word match,') leaving a dangling, unfinished thought. That truncation costs credibility and readability.

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?

No output schema exists, and the description compensates by naming the return shape (cue, category, containing sentence, and four offset units). Combined with the stated lexicon scope and language limit, an agent could call this correctly; only the cut-off sentence about match semantics is a real gap.

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 100%, so the input schema already documents 'text' and 'extraCues' fully. The description adds only that caller-supplied phrases extend the lexicon, which is a modest but real addition beyond the schema; baseline 3 applies.

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: extract hedging and certainty cues from text, returning offsets. The parenthetical example cues and the enumerated lexicon categories make the operation unambiguous without opening the schema.

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?

Scope is implied by 'Rule-based and English only' and the cue examples, and no sibling is a plausible substitute, but there is no explicit 'use this when...' or exclusion guidance. An agent must infer that this is for stylometric/evidentiality analysis rather than sentiment or grammar checking.

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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