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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

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds that it returns example questions with tool/argument shapes and can be called with no args or a topic. This adds value beyond annotations, but no critical behavioral details are missing.

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 front-loaded with common queries. It organizes topics in a parenthetical list. Could be slightly tighter, but each 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?

For a simple tool with 1 optional parameter and no output schema, the description fully covers purpose, usage, parameter behavior, and when to use. No gaps.

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%. The description adds examples of topic values ('finance', 'pharma', etc.) and explains that omitting topic gives a cross-category spread, which adds meaning beyond the schema's 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 it returns category-bucketed example questions and is the onboarding entry point. It uses specific verbs ('returns', 'use this FIRST') and distinguishes from siblings by explaining it's for when the agent doesn't know what Pipeworx can do.

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?

Explicitly states 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'. Does not explicitly state when not to use, but 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

B3.4/5.0
Disambiguation2/5

The set contains multiple near-duplicate meta-query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools) and five overlapping Polymarket analysis tools, creating genuine selection ambiguity despite detailed descriptions. The single events tool is distinct, but it is drowned out by a crowd of similar data-research utilities.

Naming Consistency4/5

Almost all tools follow a consistent lowercase snake_case verb_noun pattern (ask_pipeworx, compare_entities, validate_claim, remember, forget). Only 'events' deviates by being a bare noun, but the overall convention is predictable and readable.

Tool Count1/5

32 tools is extreme for a server named 'Montreal Events', and only one tool actually relates to that domain. The remaining 31 form a sprawling, unrelated data-research, prediction-market, and memory toolkit that would overwhelm any agent trying to work with Montréal event data.

Completeness1/5

For the server's stated purpose, the surface is severely incomplete: a single read-only event search with no event details, venue info, categories, or management operations. The Pipeworx tools may cover their own domain thoroughly, but they contribute nothing to the Montreal Events scope.