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Pypi

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds behavioral context about the output structure (category-bucketed example questions) and that it draws from the live catalog. 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 front-loaded with the purpose and uses a single paragraph. Every sentence adds information, though it is slightly longer than necessary. It earns its place.

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 having no output schema, the description fully explains what the tool returns (example questions with tool+argument shapes), how to use it (no args or with topic), and when to use it (first when onboarding). It is complete for its 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?

The schema has one optional parameter with enum-like description. The description adds context that omitting it gives a cross-category spread and lists example topics, which adds meaning beyond the schema. Schema coverage is 100%, so baseline is 3; description adds value.

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 tool is an onboarding entry point that returns category-bucketed example questions with tool+argument shapes. It uses specific verbs like 'returns' and distinguishes itself from siblings as the first tool to use when unsure.

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 says to use this FIRST when you don't know what Pipeworx can do, and gives guidance on calling with no arguments for full spread or passing a topic to focus. It does not explicitly exclude alternatives, but the context is clear.

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 some overlap exists among the polymarket tools (e.g., bet_research, polymarket_arbitrage, polymarket_edges) and the ask_pipeworx variants. The detailed descriptions help distinguish them, but an agent might still misselect in those groups.

Naming Consistency4/5

Tool names follow a mostly consistent verb_noun pattern in snake_case. Minor deviations exist, such as 'remember' vs 'recall' and the mixed use of verbs and nouns (e.g., 'ask_pipeworx' vs 'polymarket_arbitrage'), but overall the pattern is predictable.

Tool Count3/5

With 35 tools, the server is on the heavy side. While each tool serves a specific purpose, the sheer number may be overwhelming, and some subsets (like the 7 polymarket tools) could potentially be consolidated. Still, the scope justifies many of them.

Completeness4/5

The server covers a wide range of functionalities: Python package management, company research, prediction markets, monitoring, memory, and data queries. Minor gaps exist (e.g., no direct tool for editing subscriptions), but the surface is generally comprehensive and well-rounded.

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