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Macro Snapshot

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds caching behavior (5min-1h) and data source (CF analytics-engine, no PII), which provides useful 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.

Conciseness5/5

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

Two concise paragraphs with front-loaded main output. Every sentence adds value without 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?

For a simple read-only tool with one optional param and no output schema, the description covers purpose, usage, caching, data source, and use cases completely.

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 one param with enum and description. The description adds semantic nuance: shorter windows for hot trends, longer windows for steady-state demand, enhancing understanding beyond the enum.

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 window. It distinguishes from siblings like discover_tools by focusing on real-time popularity data.

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 given, covering discovery, validation, and alignment. It does not explicitly state when not to use, but the context is sufficient.

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

A3.9/5.0
Disambiguation3/5

Several clusters overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions, and ai_visibility_check duplicates scan_competitor_ai_presence at a smaller scale. The detailed descriptions do separate most of these by routing, mode, or output, but the currently identical beta router and the broad ask/research family create real ambiguity.

Naming Consistency3/5

Names are all lowercase snake_case and there are coherent prefixes like polymarket_ and pipeworx_, but the set mixes imperative verb_noun names (list_subscriptions, validate_claim) with descriptive noun phrases (macro_snapshot, entity_profile, polymarket_edge_tracker) and bare verbs. The inconsistency is readable but not a single predictable pattern.

Tool Count2/5

33 tools is well beyond the 25+ threshold for a coherent server, and the set spans many unrelated domains: data routing, prediction markets, memory, subscriptions, AI visibility, package scanning, and llms.txt generation. Even if each cluster has a purpose, the server is overloaded and several high-level wrappers could be consolidated.

Completeness4/5

The main clusters are well covered: memory has remember/recall/forget, subscriptions have subscribe/list/recent_alerts/unsubscribe, and company research has resolve_entity, entity_profile, recent_changes, and compare_entities. Minor gaps exist (no direct trade placement, no general web search, no account/profile management), but agents can complete most workflows without dead ends.

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