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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds significant context: it returns example questions with exact tool+argument shapes from a live catalog, and lists categories. This fully describes behavior 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.

Conciseness5/5

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

Every sentence adds value: starts with example questions, states purpose, lists categories, gives usage instructions. No fluff, well-structured, appropriately sized for the complexity.

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?

With no output schema, the description fully covers return format (category-bucketed examples with tool+argument shape). It also mentions live catalog and meta-tools. Annotations cover safety. Complete for a discovery tool with one optional parameter.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description reaffirms the topic parameter's purpose and optionality but does not add new semantic information beyond the schema. It provides usage context, which is more relevant to usage guidelines than parameter semantics.

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 the tool returns category-bucketed example questions about Pipeworx capabilities, with specific verb+resource and explicit differentiation from siblings like 'discover_tools' by positioning it as the onboarding entry point.

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 explains when to use this tool (first interaction, when unsure of capabilities, to learn meta-tools) and provides two usage modes: full spread with no arguments or focused with a topic. No exclusions needed, but the context is clear.

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

A3.9/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and suggest_questions all route questions to the same underlying data catalog, making it hard to pick the right one. The Polymarket-related tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) also have overlapping discovery and analysis purposes.

Naming Consistency3/5

Many tools use descriptive snake_case, and the ask_pipeworx family shares a clear prefix, but the set mixes generic memory verbs (remember, recall, forget), brand-prefixed tools (here_*, pipeworx_*), and standalone names like bet_research and scan_dependency. There is no consistent verb_noun pattern across the whole server.

Tool Count2/5

35 tools is heavy for a single MCP server, and a large portion are meta-tools layered over the same 5,752-tool catalog (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions). The broad intentional scope explains the count, but the tool surface feels bloated and harder to navigate than it needs to be.

Completeness4/5

The domain is unusually broad—data querying, entity resolution, comparison, monitoring, memory, geolocation, prediction markets, dependency scanning—and the set covers most workflows end to end. Minor gaps exist, like no direct pipeworx:// citation fetcher and no update/list/delete pattern for entity profiles, but the core user journeys are well supported.

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