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Quotes

by io.github.pipeworx-io

Get Scene

get_scene
Read-onlyIdempotent

Return the full text of a specific scene from a play (every speech, every line, in order). Example: author_id="william-shakespeare", work_slug="hamlet", act=3, scene=1 returns the entire "To be, or not to be" scene including all of Hamlet's soliloquy and the subsequent dialogue with Ophelia. Useful for context, citation, or close reading.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actYesAct number.
sceneYesScene number.
author_idYesAuthor id (e.g., "william-shakespeare").
work_slugYesWork slug (e.g., "hamlet").

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and mutability. The description adds that it returns lines in order, but does not disclose any behavioral traits beyond what annotations and schema imply.

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?

The description is three sentences plus an example, no wasted words. The main action is front-loaded, followed by a compelling example and use cases.

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 that an output schema exists, the description does not need to explain return values. It sufficiently covers what the tool does, how it works (full text in order), and when to use it.

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?

The input schema already describes all four parameters with 100% coverage. The description's example provides concrete values (e.g., author_id='william-shakespeare') which adds context but does not significantly augment the schema.

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 it returns the full text of a specific scene, including every speech and line in order. The concrete example with Hamlet's 'To be, or not to be' scene makes the purpose unmistakable. It distinguishes itself from siblings like get_act and get_chapter.

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 mentions the tool is 'useful for context, citation, or close reading,' providing clear use cases. It does not explicitly state when not to use it or list alternatives, but the sibling tools (e.g., get_act) imply the appropriate scope.

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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