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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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds significant context about being an onboarding tool returning categorized examples from a live catalog, adding 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?

Long but front-loaded with common queries and well-structured with categories and usage patterns. Some redundancy but overall efficient for the context.

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?

No output schema, but description fully explains return content (category-bucketed examples with tool signatures) and covers both full spread and topic-focused usage. Complete for its role.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with one optional parameter described. Description enriches with example focus areas and default behavior, but baseline 3 is appropriate as schema already does the heavy lifting.

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?

Description clearly states it is the onboarding entry point providing categorized example questions with tool+argument shapes, and distinguishes from siblings by being the first tool to use when the agent doesn't know 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?

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do' and describes calling with no arguments or with a topic, but lacks explicit when-not-to-use guidance.

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
Disambiguation2/5

Multiple tools blur together: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-variants, and the six polymarket_* tools plus bet_research heavily overlap in purpose. Descriptions are detailed and cross-reference each other, but an agent must read very long definitions to avoid misselection.

Naming Consistency4/5

The set is consistently snake_case with helpful domain prefixes like ask_pipeworx, polymarket_*, and scan_*. Deviations such as deep_research, entity_profile, recent_alerts, and the bare verbs remember/recall/forget break a strict verb_noun pattern but remain predictable.

Tool Count3/5

34 tools is heavy for one server, and several groups could plausibly be consolidated. However, the platform spans data lookup, research, prediction markets, memory, subscriptions, and utilities, so the breadth partially justifies the count.

Completeness5/5

The surface covers lookup, grounded verification, deep research, entity comparison, claim validation, prediction-market analysis, memory CRUD, subscription lifecycle, and discovery. There are no obvious dead ends, and gaps are minor or workaroundable.

Resources