Skip to main content
Glama

Openverse

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

A4.8/5.0
Behavior5/5

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds value by detailing that the output includes categorized examples with tool+argument shapes, and explains behavior with/without the 'topic' argument, going 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.

Conciseness4/5

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

The description is well-structured starting with common user queries, then purpose, output format, and usage. It's informative but slightly long; could be trimmed slightly without losing meaning.

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, the description fully covers what it returns, how to invoke it (with/without topic), and its purpose. No output schema exists, but the description adequately explains the output structure.

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% with a single parameter 'topic'. The description adds value by listing possible values ('finance', 'pharma', etc.) and explaining behavior when omitted, enriching 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 uses specific verb 'returns' and resource 'category-bucketed example questions' with exact tool/argument shapes. It clearly distinguishes from siblings like discover_tools and ask_pipeworx by stating its role 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 states '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 clear context and implicitly differentiates from other tools by positioning as starting point.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Openverse toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation2/5

Several tools occupy the same functional space: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions from Pipeworx data, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The prediction-market tools also heavily overlap in purpose, as do ai_visibility_check and scan_competitor_ai_presence. Only the Openverse media tools and memory/subscription tools are cleanly distinguishable.

Naming Consistency3/5

All names are lowercase snake_case, but the naming conventions are mixed: verb_noun tools like search_images and resolve_entity coexist with bare verbs like remember, recall, forget, and subscribe, plus noun compounds like entity_profile, polymarket_edges, and bet_research. Subfamilies such as polymarket_* and the audio/image tools are internally consistent, but there is no single predictable pattern across the full set.

Tool Count2/5

37 tools exceeds the 25+ threshold and feels inflated for the surface, especially since several could be consolidated: there are three ask_pipeworx variants and five overlapping prediction-market tools. The Openverse-specific core is only 6 tools, with 31 mostly unrelated Pipeworx and utility tools attached, making the server feel like a grab-bag rather than a purpose-built Openverse integration.

Completeness4/5

Each embedded subdomain covers its main lifecycle well: Openverse has search/get/related for images and audio, memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and research has ask, grounded, deep_research, entity_profile, compare_entities, resolve_entity, and validate_claim. Minor gaps exist—notably no Openverse video/collection tooling and no explicit memory update—but agents can work around them without hitting dead ends.

Resources