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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.

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds that it returns example questions drawn from a live catalog of thousands of tools, and mentions meta-tools. No contradictions, and it provides useful behavioral context 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 somewhat lengthy but well-structured: it starts with example queries, then explains the output and usage. It is front-loaded with key information, though slight tightening could improve conciseness.

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?

The description explains the return structure (category-bucketed example questions with tool calls) and usage patterns. Given no output schema, it covers essential information for an agent to use the tool correctly. 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 description of the 'topic' parameter. The description adds meaning by explaining the effect of omitting vs specifying the topic, and lists example topic values, providing more clarity than the schema alone.

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 that this tool returns category-bucketed example questions for Pipeworx, with exact tool and argument shapes, and is the onboarding entry point. It distinguishes from sibling tools by being the first tool to use when unsure of capabilities.

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 to use this first when the agent does not know what Pipeworx can do, and provides guidance on arguments (omit for full spread, pass topic for focus). It does not explicitly state when not to use it, but the 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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap between ask_pipeworx, ask_pipeworx_grounded, deep_research, and similar data query tools. However, detailed descriptions help agents differentiate.

Naming Consistency4/5

Names consistently use snake_case and a mix of verb_noun and noun_verb patterns. No camelCase is present, but some tools like 'generate_llms_txt' have embedded acronyms, which slightly reduces consistency.

Tool Count3/5

33 tools is high, but the server covers a wide range of functionalities (data lookups, prediction markets, RSS feeds, memory). Some tools could be combined, but the count is within reasonable limits for a comprehensive tool server.

Completeness4/5

The tool set covers major areas like entity lookups, prediction market analysis, data retrieval, and memory management. Minor gaps exist (e.g., no RSS feed deletion tool), but overall it is quite comprehensive.

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