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Jina Reader

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
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds behavioral context about the output (returns category-bucketed questions with exact tools and argument shapes drawn from live catalog) which is not in annotations, enhancing transparency.

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?

The description is well-structured with front-loaded example queries, concise sentences, and no fluff. Every sentence adds value: purpose, output description, usage instructions, and sibling differentiation.

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 simplicity (0 required params, no output schema), the description is remarkably complete: it explains purpose, when to use, how to invoke, what the output contains, and even lists the categories. Nothing essential is missing.

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 covers the topic parameter with 100% description, but the description adds usage examples ('e.g. finance, pharma, betting') and clarifies behavior when omitted. This goes beyond the schema's baseline, warranting a 4.

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 with tool+argument shapes, and positions it as the onboarding entry point. It distinguishes itself from siblings by explicitly recommending to use this FIRST when the agent doesn't know what Pipeworx can do.

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 guidance: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools...' and explains both the no-argument and topic-filtered invocations.

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
Disambiguation2/5

Multiple tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and specialized tools like entity_profile or validate_claim that can answer similar questions. This creates ambiguity for an agent trying to select the correct tool.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern, with most using a verb_noun structure (e.g., ask_pipeworx, compare_entities, resolve_entity). There are no mixed conventions or chaotic naming.

Tool Count3/5

With 31 tools, the server is on the heavy side. While each tool has a distinct purpose, the number is borderline for a coherent set and could be streamlined, especially given the overlapping functionality.

Completeness3/5

The tool set covers a wide range of query and analysis tasks, including data lookup, comparison, betting research, memory, and subscriptions. However, there are notable gaps (e.g., no update/delete for most data, no user management) and some tools seem out of place (e.g., generate_llms_txt).

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