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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 indicate read-only, idempotent, and non-destructive behavior. The description adds valuable details: data is self-aggregating from CF analytics-engine, no PII, and cached 5min-1h depending on window. 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?

The description is concise with three sentences plus a bulleted list. It is front-loaded with the main output and zero wasted words.

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 read-only tool with one parameter and no output schema, the description covers purpose, usage, behavioral details, and parameter semantics comprehensively. It leaves 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 coverage is 100% with enum descriptions. The tool description adds semantic context: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This enhances parameter 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, packs, and call volume over a recent window. It uses specific verbs and resources, distinguishing it from siblings like discover_tools by focusing on trending data derived from AI agent calls.

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?

The description explicitly lists three use cases (discovering hot data sources, confirming canonical choice, seeing alignment) and explains window tradeoffs. It does not explicitly mention when not to use or alternative tools, but the context is clear.

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
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and discover_tools all serve general data querying, while bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_edge_tracker all target prediction-market opportunities. An agent would struggle to pick the right one consistently despite long descriptions.

Naming Consistency2/5

Naming conventions are mixed: some tools use snake_case bare verbs (remember, recall, forget), some use a pipeworx_ prefix, some use tradier_ prefix, and some use descriptive phrases (generate_llms_txt, scan_competitor_ai_presence). There is no consistent verb_noun or prefix pattern across the set.

Tool Count2/5

34 tools is on the heavy side, but more importantly the count does not match the server's stated identity. The server is named Tradier, yet only 3 of 34 tools are Tradier-specific market data tools; the rest are Pipeworx research, prediction-market, memory, and subscription utilities. The scope feels bloated and unfocused.

Completeness1/5

For a server named Tradier, the surface is severely incomplete: only quote, option expirations, and option chain are provided. Missing are account info, positions, orders, historical data, watchlists, and other core brokerage/trading operations. The Pipeworx research side is broad, but the apparent trading domain has major gaps that would cause agent failures.