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Quotes

by io.github.pipeworx-io

Get Chapter

get_chapter
Read-onlyIdempotent

Return the full text of a chapter from a novel/prose work (all paragraphs in order). Example: author_id="oscar-wilde", work_slug="the-picture-of-dorian-gray", chapter=2 returns the entire chapter where Lord Henry tempts Dorian. Use list_full_text_works to discover which works are chapter-based.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chapterYesChapter number.
author_idYesAuthor id (e.g., "mark-twain").
work_slugYesWork slug (e.g., "adventures-of-huckleberry-finn").

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that the tool returns 'all paragraphs in order', which is useful behavioral context beyond the annotations.

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

Conciseness5/5

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

Two sentences plus an example, no wasted words. The purpose is front-loaded in the first sentence. The example is compact and illustrative.

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

Completeness5/5

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

Given the presence of an output schema and comprehensive annotations, the description is complete. It explains the output format (full text, paragraphs in order) and provides usage context.

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 description adds little beyond the schema. However, the example provides concrete values for parameters, which aids understanding. Baseline 3 is appropriate.

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?

The description clearly states the action: 'Return the full text of a chapter from a novel/prose work'. It specifies the resource and includes an illustrative example. Among siblings, get_chapter is distinct from get_act and get_scene, which target plays.

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?

The description advises using list_full_text_works to discover chapter-based works, providing a clear prerequisite. While it doesn't explicitly state when not to use the tool, the context is sufficient for correct selection.

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

A3.8/5.0
Disambiguation3/5

The tool set includes multiple similar tools (e.g., three ask_pipeworx variants, several Polymarket tools) that could cause agent confusion. While each has a distinct purpose, the boundaries are subtle and descriptions lean heavily on jargon, making misselection likely.

Naming Consistency4/5

All tool names use snake_case consistently. Most follow a noun_verb or verb_noun pattern, but some (e.g., ai_visibility_check, bet_research) start with a subject rather than an action, breaking a strict verb-first convention.

Tool Count2/5

With 40 tools, the server feels overloaded. The name 'Quotes' suggests a narrow focus, yet the tool set spans fact-checking, company research, prediction markets, and more. Many tools are highly specialized or meta-tools, inflating the count without clear necessity.

Completeness3/5

The server covers a wide range of use cases, from quotes and literature to financial data and prediction markets. However, there are noticeable gaps in core areas (e.g., basic CRUD for quotes beyond search and random), and the sheer breadth creates dead ends for deep workflows.

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