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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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds behavioral context by noting the content is drawn from a live catalog and that it returns tool+argument shapes, which goes beyond the annotations without contradicting them.

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 somewhat lengthy but well-structured, starting with user-facing query phrases, then explaining returns, then usage instructions. Every sentence contributes essential information, and while it could be tighter, it remains efficient for the tool's complexity.

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?

For a tool with one optional parameter and no output schema, the description is exceptionally complete. It covers when to use it, how to call it (with and without arguments), what it returns, and provides examples, leaving no significant gaps for an agent to invoke it correctly.

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%, so the schema already documents the topic parameter. The description adds value by providing concrete examples ('finance', 'pharma', 'betting') and clarifying that omitting the parameter yields a full spread, enriching the semantic understanding beyond the schema's generic 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 clearly states the tool's purpose: it returns category-bucketed example questions with exact tool and argument shapes, positioning itself as the onboarding entry point. It provides example queries ('what can I ask Pipeworx?') and distinguishes itself from siblings by being the 'FIRST' tool to use when unfamiliar with Pipeworx.

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 'Use this FIRST when you do not yet know what Pipeworx can do for you' and explains how to call with no arguments or with a topic. It does not explicitly name alternatives, but it clearly indicates when this tool is most appropriate, satisfying the 'when to use' requirement.

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

B3.3/5.0
Disambiguation2/5

The major clusters are distinct (blockchain explorer, memory, subscriptions), but several tools have unclear boundaries: ask_pipeworx_beta is currently identical to ask_pipeworx, the three ask_pipeworx variants and deep_research all route questions, and the five Polymarket tools overlap heavily on edge detection. An agent would struggle to pick the right query tool or prediction-market tool without reading very long descriptions.

Naming Consistency2/5

Naming is a mix of single-word nouns (address, block, node, transaction, stats), verb-noun snake_case (validate_claim, generate_llms_txt), and noun-phrase snake_case (entity_profile, bet_research), with no consistent style or verb convention. There is no predictable pattern an agent can generalize from.

Tool Count2/5

36 tools is well above the comfortable range, and most are meta-tools for Pipeworx, Polymarket, memory, and subscriptions rather than Blockchair blockchain functionality. A large share of the count is redundant query and edge-analysis variants, so the size adds confusion rather than capability.

Completeness4/5

As a read-only research and monitoring gateway, the surface is fairly complete: universal routing, grounded answers, entity resolution, profiles, comparisons, claim validation, memory, and subscriptions all have lifecycle coverage. Relative to the Blockchair name, the blockchain side is thin but covers address, block, transaction, node, and stats, with only minor gaps like mempool or raw script details that agents can work around.