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Dostuff

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds behavioral context: it returns category-bucketed examples with exact tool+argument shapes, drawn from a 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 somewhat long but well-structured: it starts with natural language queries the user might type, then explains functionality and parameter. Each sentence adds value, though could be slightly trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the low complexity (1 optional param, no output schema), the description adequately explains what the tool returns (category-bucketed examples with tool+argument shapes) and its role in onboarding. It is complete for its purpose.

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% with a detailed description for the 'topic' parameter. The tool description clarifies the optional nature and lists example focus areas, adding slight value beyond the schema.

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 states exactly what the tool does: returns example questions across categories to help users understand Pipeworx. It distinguishes from sibling tools like ask_pipeworx by explicitly positioning it as the onboarding entry point and recommending it be used first.

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?

The description provides explicit guidance on when to use the tool (first time connecting, to learn capabilities) and how to invoke it (no arguments for full spread, optional topic for focus). It also tells users to use it before other meta-tools.

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

The set contains near-duplicate tools (ask_pipeworx_beta explicitly 'currently matches ask_pipeworx exactly') and a dense family of six polymarket_* tools whose boundaries are subtle, plus overlapping onboarding tools in discover_tools and suggest_questions. Detailed descriptions mitigate some confusion, but several tools are difficult to tell apart without reading their full text.

Naming Consistency3/5

All names are lowercase snake_case and readable, with consistent prefix families (ask_pipeworx, polymarket_, pipeworx_), but the overall structure is mixed: bare verbs (remember, forget, subscribe), adjective+noun names (recent_alerts, recent_changes), and noun+noun domain tags (polymarket_edges, entity_profile) rather than a uniform verb_noun pattern.

Tool Count2/5

At 33 tools the set exceeds the 25-tool threshold and feels heavy, carrying an experimental duplicate of ask_pipeworx and a six-tool polymarket family that could plausibly be consolidated. The breadth reflects several unrelated domains bundled into one server rather than a focused scope.

Completeness3/5

The data-research core (ask, grounded, deep_research, profiles, comparisons, claim validation, entity resolution) and the prediction-market analysis suite are thoroughly covered, and memory plus subscription lifecycles are complete. However, the events domain the server is named for is thin (only events + metros), and the overall set lacks a single coherent purpose against which completeness can be cleanly judged.

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