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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.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already cover readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds that it returns category-bucketed example questions drawn from a live catalog, and explains the structure. No contradiction.

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 with example queries front-loaded. It is slightly long but every sentence adds value. The structure is clear and easy to scan.

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 the tool's role as an onboarding/discovery tool with no output schema, the description fully explains purpose, usage, parameters, and return structure (category-bucketed examples with tool shapes). It is complete and leaves no gaps.

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 description coverage is 100%, but the description adds value by listing possible values ('finance | pharma | ...') and explaining the behavior when omitted (full spread). This is helpful beyond the schema's generic 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 it is an onboarding entry point for an agent to know what questions to ask, returning category-bucketed example questions with tool+argument shapes. It distinguishes from sibling tools by being the first tool to use when unsure about Pipeworx's capabilities.

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 says 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools'. Provides guidance on when to pass no arguments for full spread or a specific topic to focus, with examples.

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

B3.3/5.0
Disambiguation2/5

Several tools have overlapping or intentionally duplicated purposes: ask_pipeworx/ask_pipeworx_beta currently behave identically, and the Polymarket cluster (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread) presents multiple scanners with fuzzy boundaries. The long descriptions help, but the set as a whole is hard to navigate without close reading.

Naming Consistency2/5

Naming is a mix of bare single nouns (hero, match, meta, remember, forget), snake_case verb-first names (ask_pipeworx, compare_entities, generate_llms_txt), and noun-first compounds (pipeworx_feedback, bet_research, scan_competitor_ai_presence). There is no consistent verb_noun or noun-verb convention across the set.

Tool Count2/5

At 43 tools, the server bundles at least five unrelated domains (Dota 2 stats, Pipeworx data querying, Polymarket analytics, memory, subscriptions, AI visibility). That is far too many for a focused MCP server, and the mix makes the surface feel like a grab bag rather than a purpose-built toolkit.

Completeness4/5

Each subdomain individually has solid coverage: Dota 2 has heroes/matches/players/tournaments/meta plus a GraphQL fallback, the data layer has discovery + routing + grounding + validation, and memory/subscriptions have full lifecycle operations. The only real gap is cohesion across domains; within each slice there are no obvious dead ends.

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