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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. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds behavioral details like drawing from a live catalog of thousands of tools and returning exact tool+argument shapes, but these are more feature descriptions than behavioral restrictions or side effects.

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 fairly long but front-loaded with alternative phrasings and structured logically. Every sentence contributes useful information, though a slight reduction in length 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?

Given the single optional parameter and no output schema, the description adequately explains what the tool returns (category-bucketed example questions with tool+argument shapes) and its role in onboarding. It also references meta-tools to connect with the broader tool ecosystem.

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% and the schema already describes the 'topic' parameter well. The description adds value by listing example values for the topic and explaining the behavior when omitted (cross-category spread), which goes beyond the schema's description.

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 an onboarding entry point that returns category-bucketed example questions with tool+argument shapes. It distinguishes itself from siblings by positioning itself as the first tool to use when the agent doesn't know what Pipeworx can do.

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 when to use it ('when you do not yet know what Pipeworx can do' and 'Use this FIRST'). It also mentions that calling with no arguments gives the full spread and passing a topic focuses the results. However, it does not explicitly state when not to use it or provide alternative tools for specific scenarios beyond the meta-tools list.

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
Disambiguation3/5

While many tools have detailed descriptions that help differentiate them, there is significant overlap among query tools like ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research. The prediction market tools also cluster together, making it challenging for an agent to quickly pick the right one without careful reading.

Naming Consistency4/5

Most tools follow a descriptive snake_case convention (e.g., ask_pipeworx, entity_profile, compare_entities). Minor deviations exist, such as 'ai_visibility_check' and 'deep_research', but overall the naming pattern is predictable and clear.

Tool Count3/5

With 33 tools, the server is larger than typical single-domain servers. While it supports a broad data platform, this count feels somewhat bloated and could benefit from consolidation, especially among overlapping query tools.

Completeness1/5

The server is named 'Materials' but contains only two materials-specific tools (materials_search, materials_stability). The remaining 31 tools cover unrelated domains (finance, economics, prediction markets, etc.), leaving the stated domain severely incomplete.