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Scream Void

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description's addition of output format details (category-bucketed examples with tool and arguments) enhances transparency without redundancy.

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 front-loaded with example queries and structured to explain purpose, usage, and output. While a bit verbose (multiple lines), every sentence earns its place by providing actionable guidance.

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 tool's simplicity (one optional parameter, no output schema), the description fully covers what the agent needs: when to use, how to call, what the response contains, and even lists example topics. No gaps remain.

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 clear description for the 'topic' parameter. The description adds practical nuance: omitting topic gives a full spread, while passing a topic focuses results. This goes beyond the schema's basic enumeration.

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 is the onboarding entry point that returns category-bucketed example questions with exact tool and argument shapes. It distinguishes itself from siblings by being the first tool to use when unfamiliar with Pipeworx's capabilities.

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 instructs to use this tool FIRST when the agent does not yet know what Pipeworx can do, and explains how to call it with no arguments or with a specific topic. This differentiates it from sibling tools like ask_pipeworx or compare_entities.

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

Many tools overlap significantly in purpose, especially the ask_pipeworx variants (standard, beta, grounded) and deep_research, as well as the polymarket arbitrage/edges/fill_risk/kalshi_spread suite. It would be hard for an agent to reliably choose the correct tool without deep understanding of subtle distinctions.

Naming Consistency3/5

Tool names mostly use snake_case, but there is no consistent prefix or verb pattern. Some names are descriptive phrases (e.g., scream_void_scream, compare_entities) while others are vague (e.g., forget, recall). The mix of 'pipeworx_' prefix on some tools and lack of it on others adds inconsistency.

Tool Count3/5

32 tools is on the high side for a data research server, given the overlapping functionality. Some tools could be merged (e.g., the ask_pipeworx variants, polymarket tools). However, the count is not excessive enough to be unmanageable, and each tool serves a specific niche.

Completeness4/5

The server covers a broad range of data sources and prediction market analysis, with tools for research, comparison, monitoring, and memory. Minor gaps exist (e.g., no tool to update stored memories or manage subscriptions beyond CRUD), but core workflows are well-supported.

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