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Cactus

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

A4.2/5.0
Behavior4/5

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

Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description goes beyond annotations by adding concrete context: data source (CF analytics-engine), privacy (no PII), output shape ((pack, tool, count)), and caching behavior (5min-1h). This meaningfully enriches the agent's understanding.

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 moderately sized but well-structured: a lead sentence defining the tool, a bulleted list of use cases, and a final technical note. Each sentence contributes value, though the use-case list could be slightly more compact.

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 (one optional parameter, no output schema), the description is complete. It explains what is returned, the available windows, use cases, data source, privacy characteristics, and cache freshness. No critical information is missing for an agent to decide whether to invoke it.

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

Parameters3/5

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

Schema description coverage is 100% and the parameter description already explains the window enum and its semantics (shorter for hot right now, longer for steady-state). The description adds a default ('24h') and reiterates the window options, but does not provide additional parameter-level 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 uses a specific verb ('Returns') and clearly defines the resource: top tools, top packs, and total call volume over a time window. It distinguishes from siblings by emphasizing its social-proof/trending nature ('What other AI agents are calling...'), which is unique among the sibling tools like discover_tools or recent_alerts.

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?

Provides explicit 'Useful for' scenarios (discovering hot data sources, confirming canonical tool choice, aligning with agent needs). This gives clear context for when to use it, though it does not explicitly state when not to use it or name alternative 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

A3.9/5.0
Disambiguation3/5

Most tools have clearly differentiated roles, but several pairs blur boundaries: ask_pipeworx and ask_pipeworx_beta are currently functionally identical, and identify vs resolve both wrap the same NCI CACTUS service. The detailed descriptions rescue most selections, but an agent could easily mispick between the research and chemical lookup options.

Naming Consistency3/5

Names are mostly snake_case and readable, but conventions are mixed: some are verb-first (ask_pipeworx, validate_claim, search_within) while many are noun-first or domain-prefixed (entity_profile, polymarket_edges, recent_changes, pipeworx_trending). There is no single predictable pattern for a new tool's name, though subfamilies (polymarket_*, ask_pipeworx_*) are internally consistent.

Tool Count3/5

At 33 tools, this is well above the typical well-scoped range and carries real selection overhead. The unusually broad purpose—a data router plus prediction-market analysis, memory, subscriptions, and several standalone utilities—partially justifies the count, but it still feels heavy and could be consolidated.

Completeness4/5

For its varied subdomains, coverage is strong: memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, and prediction markets span research, edge scanning, arbitrage, fill-risk, and edge telemetry. Minor gaps exist—such as no direct tool to fetch a specific citation URI by identifier, and the redundant stable/beta router pair—but there are no obvious dead ends.

Resources