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Gtin

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

A4.7/5.0
Behavior5/5

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

Adds valuable behavioral context beyond annotations: data source (CF analytics-engine), privacy (no PII), caching behavior (5min-1h). Annotations already signal safety (readOnly, idempotent, not destructive), and description complements 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?

Concise and well-structured: two sentences for core function, bulleted use cases, and a final line on data source and caching. Every sentence serves a purpose with no redundancy.

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 simple input schema (1 optional param) and no output schema, the description fully covers what the tool returns (top tools, packs, volume) and usage context. Caching and non-PII nature further inform agent behavior. 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 for the single parameter 'window' with enum and description. Description adds value by explaining the semantic difference between short and long windows (hot vs. steady-state demand). Baseline 3 elevated to 4 for extra guidance.

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 explicitly states the tool returns top tools, top packs, and total call volume over a window, with three clear use cases. It distinguishes itself from sibling tools by focusing on trending data rather than discovery or other functions.

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 explicit use cases for when to use the tool. It does not mention when not to use, but the context of trending vs. historical is implied by window choices. Slightly missing explicit exclusion criteria.

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

All Gtin toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all handle query routing, and the five polymarket_* tools plus bet_research blur the line between market scanning, edge detection, and fill-risk analysis. Several pairs (entity_profile/compare_entities/recent_changes, ai_visibility_check/scan_competitor_ai_presence) also overlap substantially.

Naming Consistency2/5

The tool names mix multiple conventions: verb_noun (validate_gtin, list_subscriptions, generate_llms_txt), brand-prefixed groups (pipeworx_*, polymarket_*, ask_pipeworx*), and bare nouns (entity_profile, recent_alerts). The server is named after GTIN/barcodes, yet most tools are branded Pipeworx or Polymarket, making the set feel incoherently named.

Tool Count2/5

33 tools is well over the threshold where a typical agent can comfortably navigate the surface, especially since they span unrelated domains: barcode validation, data lookups, prediction-market arbitrage, memory storage, subscriptions, npm scanning, and llms.txt generation. The count reflects an overgrown grab bag rather than a well-scoped toolset.

Completeness2/5

There is no coherent domain to assess completeness against: for the server's apparent GTIN/barcode purpose, only gtin_check_digit and validate_gtin exist (and not even a lookup for product data by GTIN). For the broader Pipeworx platform hinted at by most tools, the surface is scattered, with deep coverage of prediction-market edges but arbitrary one-off utilities elsewhere.

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