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Quotes

by io.github.pipeworx-io

Quote By Location

quote_by_location
Read-onlyIdempotent

Look up quotes by structural address within a work — act/scene for plays, chapter for novels. Example: author_id="william-shakespeare", work_title="Hamlet", act=3, scene=1 returns the "To be, or not to be" line.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actNoAct number (plays only).
sceneNoScene number (plays only).
chapterNoChapter number (novels/essays).
author_idYesAuthor id (e.g., "william-shakespeare").
work_titleYesTitle of the work (e.g., "Hamlet", "The Picture of Dorian Gray").

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
workYesWork title that was queried
countYesNumber of quotes found at location
quotesYesArray of quotes from the specified location
author_idYesAuthor id used for lookup

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds example of output but no additional behavioral traits like error handling or rate limits.

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?

Single sentence plus example—extremely concise and front-loaded. No redundant 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 output schema and annotations present, description is adequate for a simple lookup. Lacks details on error cases or return format, but sufficient for typical use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage with clear descriptions. Description reinforces parameter groupings (act/scene vs chapter) and provides a concrete example, adding value beyond 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?

Description uses specific verb 'Look up quotes' and resource 'structural address within a work', clearly distinguishing from sibling tools like search_quotes or random_quote. The example reinforces the purpose.

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?

Description implies usage for known structural addresses but lacks explicit when-not-to-use or alternative tool names. Context from sibling tools suggests differentiation, but not stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Quotes toolsInspect
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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.

Resources