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Pypi

by io.github.pipeworx-io

Pipeworx Trending

pipeworx_trending
Read-onlyIdempotent

What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNo24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds valuable context: caching behaviour (5min-1h), data source (CF analytics-engine), and no PII. This supplements annotations without contradiction.

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 concise (5 sentences), front-loaded with the core functionality, and structured with bullet-like use cases. Zero fluff; every sentence earns its place.

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?

The description covers return content (top tools, packs, call volume), caching, and data provenance. While there is no output schema, the description provides sufficient context for agent decision-making. Minor gap: no explicit mention of response format.

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 detailed enum values. The description adds semantic guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This clarifies the trade-off beyond the schema's static 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 clearly defines the tool's purpose: returning top tools, packs, and call volume over recent windows. It uses specific verbs ('Returns') and resources ('top tools, top packs, and total call volume'), distinguishing it from sibling tools like discover_tools or 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 Guidelines4/5

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

Three explicit use cases are provided (discovering hot data, confirming popular tools, checking alignment). While it does not explicitly state when not to use, the context makes the intended usage clear. No alternative tools are named, but the use cases guide appropriate invocation.

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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but some overlap exists among the polymarket tools (e.g., bet_research, polymarket_arbitrage, polymarket_edges) and the ask_pipeworx variants. The detailed descriptions help distinguish them, but an agent might still misselect in those groups.

Naming Consistency4/5

Tool names follow a mostly consistent verb_noun pattern in snake_case. Minor deviations exist, such as 'remember' vs 'recall' and the mixed use of verbs and nouns (e.g., 'ask_pipeworx' vs 'polymarket_arbitrage'), but overall the pattern is predictable.

Tool Count3/5

With 35 tools, the server is on the heavy side. While each tool serves a specific purpose, the sheer number may be overwhelming, and some subsets (like the 7 polymarket tools) could potentially be consolidated. Still, the scope justifies many of them.

Completeness4/5

The server covers a wide range of functionalities: Python package management, company research, prediction markets, monitoring, memory, and data queries. Minor gaps exist (e.g., no direct tool for editing subscriptions), but the surface is generally comprehensive and well-rounded.

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