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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 cover safety (readOnly, idempotent, non-destructive). The description adds behavioral context beyond annotations: it explains the output consists of category-bucketed example questions with exact tool/argument shapes, and that omitting topic returns the full spread vs focusing with topic. This is useful but doesn't fully describe output size or potential latency, so a 4 is appropriate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with sample queries, immediately establishing purpose, then efficiently covers invocation, output, and when to use. Every sentence contributes unique value—no fluff or repetition of schema/annotations.

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 there is no output schema, the description fully covers return values (category-bucketed examples with tool/argument shapes), invocation modes, and context. It tells the agent exactly what to expect and how to choose, making it complete for a tool of this simplicity.

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 topic already described. The description adds value by giving concrete examples ('finance', 'pharma', 'betting') and clarifying the default behavior ('Omit for a cross-category spread'), which extends beyond the schema's bare enum list.

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 explicitly states this is the onboarding entry point for an agent wanting to know what Pipeworx can do. It uses specific verbs like 'returns category-bucketed example questions' and distinguishes itself from siblings by positioning it as the 'FIRST' tool to use and referencing specific meta-tools to learn.

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?

Provides clear usage guidance: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools...' It also explains the optional topic parameter and when to omit it, giving explicit direction on when to use this tool vs alternatives.

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

Many tools have overlapping purposes, especially the ask_pipeworx family (4 variants) and polymarket tools (5 variants). The memory tools (remember/recall/forget) and subscription tools also overlap with each other. While descriptions provide some differentiation, the sheer number of similar tools makes it hard for an agent to quickly distinguish the right one.

Naming Consistency2/5

Most names use snake_case, but there is no consistent verb_noun pattern. Some are verb_noun (ask_pipeworx, resolve_entity), some are noun_noun (bet_research, entity_profile), and others are adjective_noun (recent_changes, pipeworx_trending). The naming is arbitrary and doesn't follow a predictable convention.

Tool Count2/5

At 33 tools, the count is high but not extreme for a broad data platform. However, the server is named 'mathjs', implying a math focus, yet only 2 tools (evaluate, convert_units) are math-related. The vast majority of tools belong to a completely different domain (data lookups, prediction markets, subscriptions), making the count inappropriate for the server's apparent purpose.

Completeness1/5

For a math server, the tool surface is severely incomplete—missing basic operations like plotting, equation solving, calculus, etc. For the actual data integration and prediction market functionality, the set is more complete, but the server name misleads. The mismatch between name and content makes the completeness score very low based on the implied domain.