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Traject Ecommerce

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

A4.8/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds details: self-aggregating from CF analytics-engine, no PII, caching policy (5min-1h). No contradictions with annotations.

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?

Extremely concise: one sentence for purpose, bullet-like use cases, and technical notes. No redundancy, every sentence adds value. Well front-loaded.

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?

Given no output schema, the description hints at return structure (top tools, top packs, total call volume) but lacks explicit format. However, the tool is simple and the description is sufficient for agent understanding.

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 covers 100% of parameters with enum and description. Description adds value by explaining the trade-off between short and long windows ('Shorter windows surface what's hot right now; longer windows show steady-state demand'), enhancing understanding 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 clearly states the tool returns top tools, top packs, and total call volume over recent windows, with specific time ranges. It distinguishes itself from siblings by focusing on aggregate trending signal, unlike other tools that retrieve specific data or perform searches.

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?

Provides three explicit use cases (discovering hot data sources, confirming canonical tools, aligning use case) and explains when to use different windows (short for hot trends, long for steady-state). No alternatives mentioned but the use cases are clear enough to guide selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Traject Ecommerce toolsInspect
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TDQS

A3.8/5.0
Disambiguation4/5

Most tools have distinct purposes, e.g., Amazon and Walmart tools are platform-specific. A few overlapping tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research are differentiated by clear usage guidance, so an agent can disambiguate with reasonable effort.

Naming Consistency3/5

Tool names mix verbs and nouns with varying styles (e.g., ai_visibility_check, compare_entities, scan_competitor_ai_presence). There is no uniform pattern like verb_noun; some are descriptive phrases. The inconsistency is noticeable but not chaotic.

Tool Count2/5

36 tools is too many for a server named 'Traject Ecommerce', as many tools cover unrelated domains (Polymarket, npm packages, SEC filings). The scope is excessively broad, making the server feel like a general-purpose plugin rather than a focused ecommerce toolset.

Completeness2/5

For an ecommerce-focused server, it only covers Amazon and Walmart product/search/reviews, missing major platforms and backend operations. The broader tool set is detailed but not ecommerce-specific, leaving obvious gaps for the intended purpose.

Resources