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Digitalnz

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

A5/5.0
Behavior5/5

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds essential details: self-aggregating signal derived from CF analytics-engine, no PII, data format (pack, tool, count), and caching duration (5min-1h depending on window). No contradictions.

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: three sentences plus a parenthetical list. It opens with the core functionality, then lists use cases, and ends with technical details. Every sentence adds unique value; no redundancy or fluff.

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 (1 optional parameter, no output schema), the description fully informs the agent: what it returns (top tools, packs, volume), data source, privacy, and caching. The agent can confidently decide to invoke this tool without ambiguity.

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?

The only parameter 'window' has an enum description in the schema. The description adds meaningful context beyond schema: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This helps the agent choose the appropriate window for its goal.

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 what the tool does: returns trending data on tool and pack usage from other AI agents, including top tools, top packs, and total call volume over selectable windows. It distinguishes itself from sibling tools like discover_tools by focusing on trending behavior rather than general discovery.

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 explicitly lists three use cases: discovering hot data sources, confirming a popular tool as canonical, and aligning use cases with current demand. It also explains the window parameter's effect (shorter for hot trends, longer for steady-state), providing clear guidance on when to use each.

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

A3.9/5.0
Disambiguation2/5

Several tools are difficult to distinguish: ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx_grounded, deep_research, and ask_pipeworx have fuzzy boundaries. The polymarket_* family plus bet_research also overlap heavily, requiring agents to carefully parse long descriptions to avoid misselection.

Naming Consistency4/5

Naming is predominantly snake_case with a verb-first pattern (ask_, search, subscribe, unsubscribe, list_) and clear prefix families like polymarket_ and pipeworx_. Minor deviations like ai_visibility_check and entity_profile use noun-first phrasing, but the overall pattern is still predictable and readable.

Tool Count2/5

33 tools is heavy for any single server, and the count is especially inappropriate given the server is named Digitalnz but only two tools (search, record) serve that domain. The rest form an unrelated grab-bag of data research, prediction-market, AI-visibility, memory, and utility tools.

Completeness3/5

The research workflow is fairly well covered: ask/grounded/deep modes, entity resolution, comparisons, claim validation, subscriptions, and alerts all exist. However, the DigitalNZ surface is nearly absent—just search and record—which is a significant gap for the declared server name, while other domains like AI visibility and npm dependencies are isolated one-offs.

Resources