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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

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds that it returns example questions with tool and argument shapes from the live catalog, but does not add significant safety or behavioral context beyond what the annotations provide. No contradictions.

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

Conciseness4/5

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

The description is a single paragraph but is packed with useful information: alternative phrasings, use case, output details, and parameter guidance. It is not overly verbose for its purpose, though a slight restructuring could improve scanability.

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 no output schema, the description fully explains the return value (category-bucketed example questions with tool+argument shapes derived from the live catalog). The parameter and usage are clearly documented. This is complete for an onboarding tool of this complexity.

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% (1 optional parameter with description), but the description adds meaning by providing example topic values ('finance', 'pharma', etc.) and explaining that omitting it gives a cross-category spread. This goes beyond the schema's brief description.

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 explicitly states this is 'the onboarding entry point' for an agent that wants to know what Pipeworx can do. It specifies the output (category-bucketed example questions with tool+argument shapes) and distinguishes itself from siblings by recommending 'Use this FIRST' and mentioning meta-tools like ask_pipeworx, entity_profile, etc.

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 clearly says when to use it: 'when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains how to call it (no args or with a topic). However, it does not explicitly say when not to use it or mention specific alternative tools beyond a list.

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

A3.9/5.0
Disambiguation3/5

Several families overlap heavily—ask_pipeworx, ask_pipeworx_beta (explicitly identical today), ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions—and the five polymarket_* tools all circle around detecting or trading edges. However, detailed descriptions and distinct scopes (single vs multi-part vs grounded vs claim verdict, scan vs arbitrage vs fill risk) keep most boundaries usable.

Naming Consistency4/5

Names are uniformly snake_case and mostly follow a clear verb_noun or resource pattern (search_articles, compare_entities, list_subscriptions, remember/recall/forget). Minor deviations exist—ask_pipeworx has no underscore and some names are product-prefixed (pipeworx_trending, polymarket_edges)—but the overall pattern is still predictable.

Tool Count2/5

35 tools is well above the 25+ threshold, and the set spans unrelated domains—GDELT news, prediction markets, memory, subscriptions, npm dependency scanning, and llms.txt generation—so it feels like several servers mashed together rather than one coherent scope.

Completeness4/5

As a broad read-only data/research toolkit, coverage is strong: ask_pipeworx routes to thousands of sources, entity/compare/recent_changes/validate cover lookups, memory lifecycle is complete, and subscriptions have create/list/read/cancel. Minor gaps exist—no article-level GDELT aggregates beyond the four news tools and no direct update/delete for llms.txt—but there are no critical dead ends.