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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

A4.7/5.0
Behavior5/5

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

Annotations already declare it's read-only, idempotent, non-destructive. Description adds valuable context: data sourced from CF analytics-engine, no PII, caching behavior (5min-1h). 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?

Concise single paragraph with clear bullet-point use cases. Every sentence serves a purpose; 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?

For a simple tool with one optional parameter and no output schema, the description fully covers what is returned and how to interpret results. No gaps.

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 has 100% coverage with description for the single parameter. Description adds interpretative guidance (shorter windows for hot signals, longer for steady-state demand). Baseline 3 is elevated due to added meaning.

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 top tools, top packs, and total call volume over recent windows, with explicit use cases. It distinguishes itself from sibling tools like 'discover_tools' by focusing on trending/call volume.

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 three specific use cases (discovering hot data sources, confirming canonical choice, aligning use case). Lacks explicit exclusions or alternatives but gives strong contextual guidance.

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

The four card tools are distinct, but the majority of the server is a Pipeworx/prediction-market platform with heavily overlapping entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and discover_tools all serve similar query/discovery purposes, and ask_pipeworx_beta is explicitly an identical twin of ask_pipeworx. The six Polymarket tools and the ai_visibility/scan_competitor pair also have fuzzy boundaries that would make tool selection error-prone.

Naming Consistency2/5

All names are snake_case, but the pattern is highly inconsistent: some are verb_noun (get_card, search_cards, resolve_entity), some are bare verbs (remember, forget), some are noun-first (polymarket_edges, entity_profile, bet_research), and some are adjective_noun (recent_alerts, recent_changes). The ask_pipeworx variants share a name but differ only by suffix, which is not a clear action-oriented pattern.

Tool Count2/5

35 tools is squarely in the 'too many' territory, and the bloat is worse because the server is named Tcgdex while only 4 of 35 tools actually relate to trading cards. The remaining 31 tools form a sprawling multi-domain platform that mixes data queries, prediction markets, memory, subscriptions, AI visibility checks, and one-off utilities like generate_llms_txt and scan_dependency.

Completeness4/5

For the TCGdex card surface, the read-only workflows are covered well: search_cards leads to get_card, and list_sets leads to get_set, with no obvious dead ends. For the broader Pipeworx functionality, the set includes discovery, query, grounding, entity resolution, memory, subscriptions, and feedback, so the main workflows are supported—though the overall scope is sprawling rather than focused.