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What Can I Ask Pipeworx?

suggest_questions
Read-onlyIdempotent

What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass topic (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNoOptional focus area: finance | pharma | economics | real-estate | betting | weather | government | science | news. Omit for a cross-category spread.

TDQS

A4.5/5.0
Behavior4/5

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

Description adds valuable context beyond annotations: it explains the tool is an onboarding entry point that returns categorized examples with tool+argument shapes, and that topic narrows the spread. No contradictions with 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?

Description is efficiently front-loaded with common user questions, then explains output and optional argument. Every sentence adds value with no redundancy.

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?

Despite no output schema, the description fully explains the return type (categorized example questions with tool+argument shapes) and the effect of the optional parameter, which is sufficient for a read-only onboarding tool.

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 coverage is 100% with a description listing topic values. The description clarifies that omitting topic gives a full cross-category spread, adding meaning beyond the schema's 'focus area' hint.

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 that the tool returns category-bucketed example questions with tool+argument shapes, distinguishing it from siblings by positioning it as the onboarding entry point for new users.

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?

Explicitly says to use it first when unsure of Pipeworx's capabilities and to learn meta-tool calls, providing clear context and optional topic filtering, but lacks explicit 'when not to use' guidance.

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

Many tools serve overlapping research purposes (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim, entity_profile, compare_entities), which could confuse an agent. However, descriptions help differentiate them by use case (single vs multi-part, grounded vs standard, etc.). Some overlap remains.

Naming Consistency3/5

Tool names mix patterns: some are verb_noun (ask_pipeworx, bet_research), others are noun_phrase (price_feed, recent_alerts) or adjective_noun (ticker_v2). No strong naming convention, but all use snake_case consistently, making them readable.

Tool Count3/5

40 tools is on the high side for a single server, covering both Gemini exchange data and Pipeworx's broad knowledge tools. While each tool serves a purpose, the scope feels broad, and some tools could be separated into dedicated servers.

Completeness4/5

The Gemini exchange tools cover essential read-only data (order book, candles, ticker, trades), but lack order placement, likely intentionally. The Pipeworx tools provide extensive research capabilities across many domains, leaving few gaps for the stated purposes.