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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?

Beyond the annotations (readOnly, idempotent, non-destructive), the description adds valuable behavioral context: it discloses the data source (CF analytics-engine), privacy (no PII), output shape (pack, tool, count), and caching (5min-1h depending on window). This goes well beyond the structured hints.

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 compact and well-structured: a topic sentence, return summary, numbered use cases, and behavioral notes. Every sentence earns its place, and the most important information is front-loaded.

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?

With one optional parameter, no output schema, and strong annotations, the description is sufficient. It explains the return content (top tools, top packs, total call volume), the data shape, caching behavior, and appropriate use cases. No critical information is missing.

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?

The schema already fully documents the window parameter with enums and trade-offs, so the baseline is 3. The description adds extra meaning by relating cache duration to the window choice ('Cached 5min-1h depending on window'), which is not in the schema. This additional detail justifies a 4.

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 what the tool does: it shows what other AI agents are calling on Pipeworx, returning top tools, top packs, and total call volume over configurable time windows. This distinguishes it from siblings like discover_tools and search_datasets by focusing on aggregated agent usage.

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 'Useful for' list provides three explicit use cases: discovering hot data sources, confirming a canonical tool, and checking alignment with other agents' needs. However, it does not explicitly state when not to use the tool or mention alternatives, so it falls short of full exclusions.

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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping or redundant purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical in routing, and several polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) all surface trading opportunities with similar outputs. The three ArcGIS tools are distinct but buried among dozens of unrelated data/meta tools, making selection confusing.

Naming Consistency3/5

All names use snake_case, which is consistent, but the verb/noun pattern is inconsistent. Some are verb_noun (query_layer, resolve_entity), some are noun phrases (entity_profile, polymarket_edges, recent_alerts), and some are bare verbs (recall, remember, forget, subscribe). The naming style is readable but not predictably patterned.

Tool Count1/5

34 tools is already high, but the severe issue is that only 3 of them (search_datasets, query_layer, layer_info) relate to the server's stated ArcGIS Delaware County purpose. The other 31 are Pipeworx data, memory, subscription, and prediction-market tools, which is a blatant scope mismatch. The tool count is not appropriate for the advertised server domain.

Completeness1/5

For an ArcGIS Delaware County GIS server, the surface is extremely thin: only search, query, and layer metadata exist. There are no tools for editing features, uploading data, managing layers, or exporting maps. Conversely, the Pipeworx tools form a broad but fragmented domain with many monitoring and meta-tools but no clear end-to-end workflow. The set is severely incomplete for its apparent dual purpose.