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Data Denver

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 declare readOnly, idempotent, openWorld. Description adds return structure details (category-bucketed examples with tool shapes) and explains behavior (full spread vs topic focus). Adds useful context 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 thorough but could be trimmed; it front-loads the core purpose and lists categories efficiently. Given the complexity, it strikes a good balance.

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?

No output schema exists, but description fully explains return format (category-bucketed examples with tool/argument shapes) and covers use cases (getting started, learning meta-tools). Complete for an onboarding tool.

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 parameter description. Description adds value by clarifying default behavior ('Omit for a cross-category spread') and reiterating the enum focus areas.

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 defines the tool as an onboarding entry point that returns category-bucketed example questions with tool and argument shapes, distinguishing it from query siblings like ask_pipeworx.

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 to use this first when unsure of what Pipeworx can do, and mentions passing topic to focus, with clear alternatives like meta-tools.

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

A4/5.0
Disambiguation3/5

Tools are richly described with clear use-cases, but several clusters overlap: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research are all question-routing tools with similar names, and the five polymarket_* tools cover adjacent prediction-market analysis. An agent could easily pick the wrong one without reading the full descriptions.

Naming Consistency4/5

All names are snake_case and mostly follow a verb_noun or domain_noun pattern (compare_entities, resolve_entity, denver_query, polymarket_edges, pipeworx_trending). There are minor deviations like single verbs (remember, forget), adjective-first names (recent_alerts, deep_research), and domain-prefixed groups, but the overall style is predictable and readable.

Tool Count2/5

34 tools is a large surface for one server, pushing beyond the 25+ threshold where agents struggle to choose. While the platform is genuinely multi-domain (data lookup, prediction markets, Denver open data, memory, subscriptions, npm checks, llms.txt generation), several niche clusters like the 5-tool Polymarket suite and 3-tool memory trio inflate the count.

Completeness4/5

The server covers its domain well: general querying, grounded verification, deep research, entity resolution, company profiling, change feeds, subscription lifecycle (subscribe/list/unsubscribe/alerts), memory lifecycle (remember/recall/forget), and discovery (discover_tools, suggest_questions). Minor gaps exist—such as no direct browsing of all Pipeworx sources—but agents can work around them.

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