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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

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds contextual detail about data provenance (CF analytics-engine), no PII, and caching behavior (5min-1h), which goes beyond the annotations and helps the agent understand freshness and privacy.

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 well-structured with a clear opening statement, numbered use cases, and a compact note on data provenance and caching. Every sentence contributes value, and the length is appropriate for the information conveyed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one optional parameter and rich annotations, the description is nearly complete. It explains the returned content (top tools, packs, total volume), data source, no PII, and caching. A minor gap is the lack of a detailed response structure, but this does not impede correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The sole parameter 'window' is fully documented in the schema with an enum and detailed semantics about short vs. long windows. The tool description only briefly mentions the window options, adding no new meaning 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 total call volume over a recent window, using a specific verb and resource. It distinguishes itself from siblings by focusing on aggregate trending data rather than individual lookups.

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 'Useful for' scenarios, providing clear context on when to use the tool, such as discovering hot data sources and confirming canonical tools. It does not name alternative tools or explicitly state exclusions, but the use cases are clear enough.

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

A3.7/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim all handle natural-language data queries, with ask_pipeworx_beta currently identical to ask_pipeworx. discover_tools and suggest_questions both exist to help agents find tools, and the five Polymarket tools have subtle, hard-to-distinguish boundaries. Agents will frequently select the wrong tool without careful reading.

Naming Consistency4/5

Tool names are mostly lowercase snake_case with verb-noun structure (query_layer, search_datasets, resolve_entity), which is consistent and readable. However, some names break the pattern (entity_profile, layer_info, recent_changes, pipeworx_feedback) and the prefixes are not uniform. Still, the convention is predictable enough to navigate.

Tool Count2/5

34 tools is a heavy count, and the vast majority are unrelated to the server's stated 'Arcgis Lacounty' purpose. Only three tools (search_datasets, query_layer, layer_info) serve the named GIS domain, while the rest form a sprawling collection of data-lookup, prediction-market, and utility tools. This is a severe scope mismatch.

Completeness1/5

The tool surface is severely incomplete for an ArcGIS LA County server: no layer listing beyond keyword search, no metadata endpoints, no editing, no spatial operations. The broader tool set lacks a coherent domain, making coverage impossible to assess beyond noting the glaring absence of core GIS functionality.