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Ror

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. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds substantial context: it returns category-bucketed examples with exact tool and argument shape drawn from a live catalog, and mentions it covers thousands of tools. No contradiction with annotations.

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 front-loaded with example queries, making it immediately recognizable, then delivers the core purpose. It is a bit long and run-on, but every sentence carries useful information. Slight structural improvement would make it more scannable, so not a 5.

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 this is a discovery/onboarding tool with no output schema, the description fully covers what it does, when to use it, how to call it, and how it relates to meta-tools. It answers the likely agent questions about purpose and invocation without needing further detail.

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% (the `topic` field description lists all allowed values and the omit behavior). The description adds minor redundancy with 'call with no arguments for the full spread' and a few examples, but does not meaningfully go beyond the schema. Baseline 3 is appropriate.

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 is specific and actionable: it states the tool 'Returns category-bucketed example questions' and positions itself as the 'onboarding entry point' for an agent. It also explicitly lists meta-tools (ask_pipeworx, entity_profile, compare_entities) to learn from, which distinguishes it from siblings.

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?

The description provides explicit usage guidance: 'Use this FIRST when you do not yet know what Pipeworx can do for you' and 'to learn how to call the meta-tools'. It also details the parameter behavior, saying 'Call with no arguments for the full spread, or pass `topic`'.

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

A3.5/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, deep_research overlaps with ask_pipeworx, and discover_tools/suggest_questions both serve a discovery role. The extremely detailed descriptions help, but the boundaries between meta-tools and research tools are genuinely confusing, especially with 34 tools in one namespace.

Naming Consistency2/5

Naming is a mix of bare verbs (get, search, recall), nouns (affiliation, entity_profile, recent_changes), and verb_noun phrases (resolve_entity, compare_entities, validate_claim). Some families are consistent (polymarket_*), but overall there is no uniform convention or prefix scheme, making the set feel arbitrary.

Tool Count2/5

34 tools is well above the 25+ threshold for 'too many.' While the broad data-platform scope explains some of the count, many tools are meta-utilities (feedback, trending, memory, subscription management) and there are near-duplicate variants (three ask_pipeworx forms, six Polymarket tools) that inflate the surface.

Completeness4/5

For a data-research platform the surface is impressively complete: lookup, grounded verification, entity profiles, comparisons, claim checking, subscription lifecycle, memory, and tool discovery are all covered. Minor gaps exist (e.g., no direct general-purpose web fetch, and ROR lacks create/update, which is acceptable for a curated registry), but agents should rarely hit dead ends.

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