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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.

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, idempotentHint, etc. The description adds details: call with no arguments for full spread, or pass topic to focus. It also reveals that returns include tool+argument shape from the live catalog. Adds 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 a single paragraph but well-front-loaded with trigger phrases and concise. It covers all necessary information without redundancy, though it could be slightly more structured (e.g., bullet points) for clarity.

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?

Despite no output schema, the description thoroughly explains what is returned: category-bucketed example questions with exact tool+argument shape. Mentions that it draws from a live catalog of thousands of tools. No gaps for this tool's complexity.

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 covers the single optional parameter with a description listing topic options. The description clarifies that omitting the topic returns the full spread and passing a topic focuses the results. Adds examples and usage context 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 clearly states the tool's purpose: it is the onboarding entry point that returns category-bucketed example questions with exact tool calls. It distinguishes itself from siblings by being a meta-tool for discovering what to ask.

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 advises to 'Use this FIRST' when unsure about capabilities, and mentions learning how to call meta-tools. Provides example trigger phrases and guidance on optionally focusing by topic.

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

A3.6/5.0
Disambiguation2/5

Most of the surface is dominated by overlapping meta-tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) plus paired helpers (discover_tools/suggest_questions, ai_visibility_check/scan_competitor_ai_presence), so an agent can easily pick the wrong one. The seven treasury_* tools are clearly distinct, but they are a small island in a much larger ambiguous set.

Naming Consistency3/5

Names are broadly snake_case and family-prefixed (treasury_*, polymarket_*, pipeworx_*), which helps, but the pattern is not consistently verb_noun: ask_pipeworx, bet_research, entity_profile, deep_research, search_within, and recent_changes mix verb, noun, and product-specific naming styles. Within families it is readable, but across the whole set it is inconsistent.

Tool Count2/5

37 tools is too many for a server whose label is 'Treasury Fiscal'; only about six tools are treasury-specific and the rest are unrelated research, betting, memory, and subscription utilities. This is well into the 25+ heavy range and would make tool selection expensive and error-prone.

Completeness2/5

For a Treasury/Fiscal server the surface is thin: debt, receipts, customs duty, average rates, exchange rates, and net cost cover only a slice of Treasury data. Missing obvious components such as daily yield curves, auction calendars/results, federal outlays/spending by agency or function, and tax or appropriations data; the broad ask_pipeworx router softens but does not fill these as first-class tools.