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Geoapify

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

A4.7/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context: the response includes category-bucketed examples with tool shapes, drawn from a live catalog, and the effect of omitting or providing a topic.

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 and front-loaded with usage examples. It is slightly verbose with redundant phrases like 'what can I ask?' but overall efficient for the information conveyed.

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 a simple tool with one optional parameter and no output schema, the description fully explains purpose, parameter usage, and the nature of the response. It covers all necessary context for an agent to use the tool effectively.

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

Parameters5/5

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

Schema coverage is 100% with a description for the 'topic' parameter. The description adds semantic value by listing example values ('finance', 'pharma', etc.) and clarifying that omitting the parameter returns a full cross-category spread.

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 the tool's purpose: returning category-bucketed example questions with exact tool+argument shapes. It uses specific verbs like 'Returns' and 'Call', and distinguishes itself as the onboarding entry point for discovering Pipeworx capabilities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool ('when you do not yet know what Pipeworx can do for you', 'Use this FIRST'). It provides context but does not explicitly mention when not to use it or compare with alternatives like discover_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

A3.8/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, entity_profile, compare_entities, and validate_claim, all retrieving entity data. Agents may struggle to choose correctly between them. Likewise, bet_research, polymarket_edges, and polymarket_arbitrage cover similar prediction market territory.

Naming Consistency3/5

All tool names use underscores, but the verb/noun order varies: compare_entities (verb_noun), entity_profile (noun_noun), scan_competitor_ai_presence (verb_noun), bet_research (noun_verb). Some names are overly long (scan_competitor_ai_presence). The pattern is readable but not fully consistent.

Tool Count3/5

With 31 tools, the count is high but not extreme. However, the set covers geospatial, data retrieval, prediction markets, npm scanning, and memory/feedback - too broad for a single server. Many tools feel added without clear justification, making the set feel bloated.

Completeness2/5

The geospatial subset is incomplete (missing elevation, distance matrix, isochrones). The data access tools overlap rather than form a complete API surface (e.g., no entity update/delete). Prediction market tools are numerous but redundant. The server tries to do too much and lacks depth in any area.

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