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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

A4.3/5.0
Behavior4/5

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

Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds value by explaining the exact output format (category-bucketed example questions with tool+argument shapes) and the behavior when called with no arguments vs. with a topic. This goes beyond the annotations to give a clear picture of what happens.

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 well-structured, starting with common user queries, then explaining the output, then parameter usage. Each sentence serves a purpose, but it is somewhat verbose. Front-loading effectively communicates the tool's primary use case.

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 that there is no output schema, the description adequately explains the return type (category-bucketed example questions with tool shapes). It covers the parameter and usage context thoroughly. A small gap is the lack of mention of potential limitations or empty results, but overall it is complete for the tool's 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?

The schema describes the 'topic' parameter with a list of options. The description enriches this by providing concrete examples of focus areas (finance, pharma, etc.) and stating that omitting it yields a cross-category spread. This adds practical context beyond the schema's description, though the schema coverage is already 100%.

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 is the onboarding entry point for an agent that just connected, providing example questions and tool usage. It uses specific verbs ('returns category-bucketed example questions') and distinguishes itself from siblings by positioning it as the first tool to use when the agent doesn't know 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool ('Use this FIRST when you do not yet know what Pipeworx can do for you') and how to use it (with no arguments or with a topic focus). It also mentions related meta-tools but does not exhaustively list all sibling alternatives or provide 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

B3.4/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, while ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all occupy adjacent lookup/discovery territory. The four Codewars tools are clear, but the broader set is genuinely hard to navigate.

Naming Consistency3/5

Most names follow a readable snake_case verb_noun style with useful prefixes like polymarket_ and user_, but there are one-word outliers (kata, user, forget, recall, remember) and inconsistent phrasing such as ai_visibility_check versus scan_competitor_ai_presence. The naming is mostly predictable but not uniform.

Tool Count1/5

35 tools is already heavy, and the server is named Codewars while only 4 of the 35 tools relate to Codewars. The remaining 31 tools belong to a completely different Pipeworx research/prediction-market/brand-visibility product, making the count both excessive and fundamentally mismatched to the server's stated identity.

Completeness1/5

For a Codewars server, the surface is severely incomplete: you can fetch a single kata and a user's profile, authored list, and completed list, but there is no search, no kata listing by rank/tag, no solution submission or training workflow, and no way to manage authored kata. The unrelated Pipeworx tools do not fill these gaps; they point at a different domain entirely.