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Quotes

by io.github.pipeworx-io

Check Attribution

check_attribution
Read-onlyIdempotent

Given a quote and (optionally) the author it is claimed to be by, return one of four verdicts: "verified" (genuine, with citation), "misattributed" (no primary source — popular but fake), "paraphrase_of_verified" (popular corruption of a real quote, returns the actual text), or "no_match" (not in corpus). Useful for journalists, researchers, and anyone tired of fake Mark Twain quotes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe quote text to check.
claimed_authorNoOptional: who the quote is popularly attributed to (e.g., "Oscar Wilde", "Mark Twain"). Narrows the check.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: it lists the four possible verdicts, explains that 'verified' comes with a citation, and clarifies that 'paraphrase_of_verified' returns the actual text. Annotations already declare readOnlyHint and idempotentHint, and the description aligns without contradiction.

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 two sentences, each serving a distinct purpose: the first explains what the tool does and its outputs, the second states its usefulness. No redundant or extraneous information.

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 tool's complexity (two parameters, one required, no nested objects) and the presence of an output schema, the description fully covers the tool's behavior and return values. It explains all verdicts and their implications, making it complete for agent 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 description coverage is 100%, so the schema already documents both parameters. The description adds value by explaining the role of 'claimed_author' ('narrows the check') and the overall purpose of each parameter in context, going slightly beyond the schema's basic descriptions.

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 verb ('check attribution'), the resource ('quotes'), and the output (four distinct verdicts). It distinguishes itself from siblings like 'validate_claim' or 'search_quotes' by focusing specifically on verifying popular attributions.

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 explicitly names target users and use cases ('journalists, researchers, anyone tired of fake Mark Twain quotes'). It implies when to use this tool, though it does not explicitly state when not to use or list alternative tools.

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.

Resources