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Pulsedive

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety. The description adds value by explaining the output content (category-bucketed example questions with tool+argument shapes) and that it is drawn from the live catalog, which goes beyond annotation-only context.

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 a single, long sentence with examples and usage, but every clause adds essential information. It is front-loaded with example queries and the core purpose, and while somewhat dense, it avoids redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no output schema, the description adequately explains the return type (category-bucketed example questions) and the parameter behavior. It also provides enough onboarding context for an agent. A perfect score would require additional detail on response structure or pagination, but overall it is complete for its purpose.

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 description covers 100% of the parameter, listing the allowed values. The description supplements this with concrete examples ('finance', 'pharma', 'betting') and explicitly states that omitting `topic` gives a cross-category spread, adding meaning beyond the schema.

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 as 'the onboarding entry point' that returns category-bucketed example questions with exact tool and argument shapes. It uses specific verbs ('returns', 'call') and distinguishes itself from siblings by instructing to 'Use this FIRST' when unsure of 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?

Explicitly says when to use: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also clarifies the optional `topic` parameter usage. However, it does not explicitly name alternative tools like discover_tools, so it lacks an explicit exclusionary comparison, earning a 4 rather than a 5.

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

A3.7/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., five 'ask_pipeworx' variants and multiple prediction market tools with subtle distinctions. An agent would struggle to pick the correct tool without careful reading of long descriptions.

Naming Consistency4/5

Most tools follow a verb_noun pattern with domain prefixes (pipeworx_, polymarket_, pulsedive_), but a few standalone verbs (forget, recall) break the pattern slightly. Overall consistent within groups.

Tool Count3/5

33 tools is on the heavy side, but the server covers a broad range of domains (data querying, prediction markets, security, subscriptions). The count is borderline but not excessive given the scope.

Completeness4/5

The tool set covers a wide array of operations: querying, entity analysis, comparisons, prediction market edge detection, subscriptions, memory, and scanning. Minor gaps exist (e.g., limited to Polymarket/Kalshi for prediction markets), but overall the surface is comprehensive.