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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds caching details (5min-1h), data source (CF analytics-engine), and privacy assurance (no PII), providing valuable behavioral context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with bullet points for use cases and clear sections. While informative, it is slightly verbose; could be tightened slightly without losing clarity.

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?

The tool is simple with one optional parameter. The description fully explains what is returned (top tools, packs, call volume) and caching behavior. No output schema is needed given the clarity. Complete for the tool's complexity.

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 a description for the 'window' parameter. The tool description adds the purpose of each window choice (hot trends vs steady-state demand), enhancing semantic 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 a window. It specifies the verb 'returns' and the resource 'what other AI agents are calling on Pipeworx'. This distinguishes it from siblings like 'get_trending' by focusing on AI agent usage trends.

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 explicit use cases are listed, providing clear guidance on when to use the tool. The description explains window choices (shorter for hot trends, longer for steady-state) but does not explicitly mention alternatives or when not to use it, missing some differentiation from similar tools.

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 are near-duplicates or have heavily overlapping responsibilities (ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded, polymarket_arbitrage / polymarket_edges / polymarket_fill_risk, and ai_visibility_check / scan_competitor_ai_presence). The many meta/entry-point tools (discover_tools, suggest_questions, pipeworx_trending) also blur the boundary between discovery and execution.

Naming Consistency2/5

Tool names mix imperative verb-first patterns (get_coin, search_coins, validate_claim) with noun-phrase labels (bet_research, entity_profile, pipeworx_trending, polymarket_edge_tracker) and inconsistent prefixes. Snake_case is consistent, but the naming grammar and verb styles are not.

Tool Count2/5

35 tools is well past the comfortable range, and the count feels inflated by duplicate routing modes, overlapping polymarket scanners, and generic memory/meta utilities. A server nominally named Coingecko carries only 4 crypto tools while the overwhelming majority belong to unrelated domains.

Completeness2/5

As a CoinGecko server, the surface is severely incomplete: search/get/market/trending exist but historical prices, OHLC, exchanges, categories, and coin details are missing. As a broader data/prediction-market utility it is more expansive, but the lack of a coherent domain makes coverage impossible to assess as a single product.