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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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context: it returns 'category-bucketed example questions... with the exact tool + argument shape' and draws from a live catalog. This adds value beyond annotations.

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 substantial paragraph that front-loads alternative phrasings and then explains functionality. It is efficient but not extremely concise; every sentence adds value.

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 the annotations, schema coverage, and lack of output schema, the description fully explains what the tool does, when to use it, what arguments it accepts, and what to expect in return. It is complete for an onboarding tool.

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%, but the description adds meaning beyond the schema by listing example topic values and explaining the effect of omitting the parameter. It clarifies usage patterns.

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 specifies a clear verb-resource combination: returns example questions categorized by topic. It explicitly lists use cases like onboarding and discovering capabilities, and distinguishes from siblings by stating 'Use this FIRST when you do not yet know what Pipeworx can do for you.'

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 provides explicit usage guidance: call with no arguments for full spread, or pass 'topic' for focus. It positions the tool as the first step for new users. However, it does not explicitly state when not to use it or name specific alternatives.

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

A3.8/5.0
Disambiguation2/5

Several clusters of tools are hard to tell apart: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve overlapping query paths, and the five polymarket_* tools plus bet_research cover heavily overlapping prediction-market analysis. Some pairs are nearly identical in purpose, like ai_visibility_check vs scan_competitor_ai_presence, and the descriptions must be read closely to avoid misselection.

Naming Consistency2/5

All names are snake_case, but the naming style is highly inconsistent across the set: some use verb_noun (ask_pipeworx, generate_llms_txt, resolve_entity), some are noun phrases (entity_profile, pipeworx_feedback, recent_alerts), and some use a vendor prefix without a clear verb (polymarket_edges, polymarket_edge_tracker). The pattern shifts between domain-specific prefixes (polymarket_*, pipeworx_*) and generic verbs with no predictable rule.

Tool Count2/5

32 tools is too many for a cohesive server, especially when the surface sprawls across unrelated domains: data querying, prediction markets, memory, subscriptions, npm scanning, AI visibility checks, and llms.txt generation. Many tools could be consolidated (the ask_pipeworx family, the polymarket family, the entity-comparison family), which would make the count feel more justified.

Completeness4/5

Within its apparent purpose as a broad data-and-research assistant, the tool set is fairly complete: it covers entity resolution, lookup, grounded verification, deep research, comparisons, memory CRUD, subscription lifecycle, discovery, and feedback. Minor gaps exist, such as no direct tool to fetch a record by its pipeworx:// citation URI (search_within implies fetching happens elsewhere) and no evident update operation for stored memories beyond save/delete.

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