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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

A4.7/5.0
Behavior4/5

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

Annotations already cover readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral detail: it returns questions drawn from a live catalog, explains topic effect, and mentions it's for learning meta-tools. 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?

Description is front-loaded with natural language questions but is slightly long. Every sentence earns its place, but could be tightened slightly without losing clarity. Well-structured.

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, description thoroughly explains the return value (category-bucketed example questions with tool/argument shapes) and how to use topic. Covers all necessary context for a simple onboarding tool.

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% with a description for the topic parameter. Description adds significant value: lists example topics, explains effect of omitting, and provides context beyond schema (e.g., 'finance', 'pharma'). Fully compensates.

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 the onboarding entry point for new users, returning category-bucketed example questions with tool/argument shapes. It distinguishes from siblings like ask_pipeworx and discover_tools by specifying its exact role.

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 tells when to use: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains optional topic parameter for focus, guiding appropriate use.

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 distinct purposes, but some overlap exists between ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim, which could cause confusion. However, the descriptions help clarify when to use each.

Naming Consistency3/5

Tool names use a mix of patterns (verb_noun, noun_noun, adjective_noun) but are consistently lowercase with underscores. Some names are vague like 'forever' and 'recent_alerts', but overall readable.

Tool Count3/5

32 tools is on the high side for a single server, but many are specialized and serve a broad data query platform. Some tools are meta-tools covering multiple use cases, which could reduce the need for so many.

Completeness4/5

The tool set covers a wide range of functionalities including data querying, entity resolution, comparisons, verification, memory, subscriptions, and prediction markets. Minor gaps like data export are not critical for its purpose.

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