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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 read-only, open-world, idempotent, non-destructive. Description adds source (CF analytics-engine), no PII, caching behavior (5min-1h), and data format (pack, tool, count), providing useful behavioral context 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?

Description is concise (5 sentences), front-loaded with purpose, and every sentence provides essential information about returns, use cases, source, and caching. No redundant or missing elements.

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?

Despite no output schema, the description explains return types (top tools, top packs, total call volume) and data structure. Caching behavior is noted. Minimal gaps for a simple trending tool, though pagination or result limits are not mentioned.

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 description on the only parameter. The description enriches by explaining window semantics (short vs long windows surface hot vs steady-state demand), adding value beyond the enum 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 states the tool returns trending tools/packs and call volume over a window, using specific verbs and resources. It distinguishes itself by focusing on aggregate agent usage rather than individual queries, though explicit sibling differentiation is absent.

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 concrete use cases are provided (discovering hot data sources, confirming canonical choice, checking alignment). Use context is clear, but no explicit when-not-to-use or alternatives are mentioned.

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

A3.6/5.0
Disambiguation3/5

The set is organized into clusters (Pipeworx querying, Polymarket analysis, entity research, subscriptions, memory), but several tools within a cluster have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, deep_research, and validate_claim all answer questions, and polymarket_edges, bet_research, and polymarket_arbitrage all surface trading opportunities. The very detailed descriptions help an agent choose correctly, but the boundaries are not crisp enough for a 4.

Naming Consistency3/5

Most names are lowercase snake_case and there are consistent prefixes like polymarket_ and pipeworx_, which aids predictability. However, the verb/noun ordering is inconsistent across the set (ask_pipeworx, bet_research, entity_profile, duffel_flight_search, ai_visibility_check), and some names are noun-heavy while others are verb-first. It is readable but not a uniform convention.

Tool Count2/5

At 32 tools the surface feels heavy for a coherent server; the rubric treats 25+ as too many. Several tools are wrappers or variants of the same underlying capability (ask_pipeworx_beta, polymarket_edges vs bet_research, ai_visibility_check vs scan_competitor_ai_presence), so the count overstates real functional breadth.

Completeness3/5

The research/data side is very complete: querying, grounded verification, deep research, entity resolution, comparison, profiles, monitoring, memory, and feedback are all covered. However, the Duffel flight tool only searches and never books, so if the server is meant to be a flight agent there is a notable dead end; the broader toolkit also lacks direct CRUD for most resources beyond subscriptions.