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by io.github.pipeworx-io

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

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, etc. Description adds details about returning exact tool+argument shapes and the effect of omitting vs. providing topic, 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 front-loaded with purpose and usage, every sentence adds value, and it is well-structured despite moderate length.

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?

No output schema exists, but the description adequately explains return values (category-bucketed questions with tool+argument shape), covers parameters, and provides usage context.

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

Parameters5/5

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

Schema coverage is 100%, and the description enriches the topic parameter with specific examples (e.g., 'finance', 'pharma') and explains behavior when omitted.

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 is an onboarding entry point that returns category-bucketed example questions, distinguishing it from sibling tools like ask_pipeworx or discover_tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises to use this tool first when unfamiliar with Pipeworx, and explains how to use it with or without the topic parameter.

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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is some overlap among the meta-querying tools like ask_pipeworx, ask_pipeworx_grounded, deep_research, and discover_tools, which could cause confusion for an agent deciding which to use.

Naming Consistency3/5

Tool names use a mix of verb_noun and noun patterns, with snake_case throughout but no single consistent structure (e.g., ask_pipeworx vs. bet_research vs. dataset). The naming is readable but not uniform.

Tool Count4/5

At 33 tools, the count is on the higher side but justifiable given the broad scope of the Pipeworx platform, covering data querying, entity analysis, prediction markets, memory, subscriptions, and feedback.

Completeness4/5

The toolset covers a wide range of data sources and operations, including querying, entity profiling, comparisons, prediction market analysis, and monitoring. Minor gaps exist (e.g., no direct SEC filing viewer), but the meta-tools handle these adequately.

Resources