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Data Kcmo

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 indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds: 'Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.' This provides useful behavioral context beyond annotations, though annotations cover the main safety profile.

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?

Description is concise, well-structured with bullet-like use cases, and every sentence adds value. No wasted words; front-loaded with the core function.

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?

Given only one optional parameter, no output schema, and strong annotations, the description provides sufficient context: what it returns, use cases, caching, and data origin. No missing information for an agent to use it correctly.

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?

Single parameter with enum (24h, 7d, 30d) and 100% schema coverage. Description adds semantic value: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This helps the agent choose appropriately.

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?

Description clearly states verb+resource: 'returns the top tools, top packs, and total call volume over a recent window.' It distinguishes from sibling tools (e.g., ask_pipeworx, discover_tools) by being an aggregation/trending tool, not a Q&A or search tool.

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?

Explicitly lists three specific use cases (discovering hot data sources, confirming canonical tool, aligning use case). Mentions caching behavior but does not explicitly state when not to use or compare to alternatives. However, the use cases provide strong guidance.

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
Disambiguation2/5

The ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research cluster is highly overlapping—beta is explicitly identical right now and grounded differs mainly in answer extraction. Several other pairs (ai_visibility_check vs scan_competitor_ai_presence, and the six prediction-market tools) also blur boundaries, making misselection likely.

Naming Consistency3/5

Names are uniformly snake_case and benefit from clear prefixes (ask_pipeworx_, kcmo_, polymarket_, pipeworx_). However, conventions are mixed between bare verbs (forget, recall, remember), noun phrases (entity_profile, polymarket_edges, recent_alerts), and verb_noun forms, and similar names like polymarket_edges vs polymarket_edge_tracker add confusion.

Tool Count2/5

34 tools is well into the bloat range, and only 3 are actually Kansas City-specific despite the server name. The surface bundles prediction markets, memory, feedback, llms.txt generation, and npm scanning alongside data lookup, making it heavy and unfocused; several meta-tools could be collapsed.

Completeness4/5

As a read-only data research platform, the surface is quite complete: discovery, querying, grounded answers, entity resolution, profiles, comparisons, claim verification, subscriptions, and memory are all covered with few dead ends. Minor gaps exist—no subscription update, no direct citation-URI fetch tool, and a thin KC-specific set—but agents can work around them.

Resources