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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

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses meaningful behavioral traits: data source ('derived from CF analytics-engine'), privacy ('no PII'), and caching ('Cached 5min-1h depending on window'). This goes beyond what annotations convey and adds valuable context for the agent.

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: it opens with a clear one-sentence summary, then details return contents, then lists three concrete use cases, and ends with technical context. Each sentence earns its place without fluff, and the size is appropriate for the tool's complexity.

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 the tool's simplicity (one optional parameter, read-only, no output schema), the description covers everything an agent needs: what data is returned, the time windows, the use cases, the data source/privacy, and caching. No critical context 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 describes the 'window' parameter with enum values and semantics. The description adds extra meaning by linking the window to cache freshness ('Cached 5min-1h depending on window') and by reinforcing that shorter windows show recent demand vs. longer windows show steady-state. This exceeds the schema baseline, warranting 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: 'Returns the top tools, top packs, and total call volume over a recent window.' It identifies the resource (Pipeworx usage data) and the action (returns trending info), and it distinguishes itself from siblings by focusing on aggregate agent-call trends rather than individual queries or discovery.

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 provides explicit use cases: discovering hot data sources, confirming canonical choices, and alignment checks. It gives clear context on when to use the tool, though it does not explicitly name alternatives or when-not-to-use cases. Since it says 'Useful for' with specific scenarios, it earns a 4 but misses the top score.

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

A3.8/5.0
Disambiguation2/5

Several clusters of tools heavily overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions over the same underlying sources, and six polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) have blurry boundaries. The four art museum tools are entirely unrelated to the data-research tools, adding confusion to the set.

Naming Consistency3/5

Names are consistently snake_case and generally readable, but the verb-object pattern is not consistent: bare verbs (remember, forget, recall, subscribe) sit alongside verb-first names (get_artwork, validate_claim, resolve_entity) and noun-first compounds (pipeworx_trending, polymarket_edges, ai_visibility_check). The repeated prefixes (ask_pipeworx, polymarket_) do provide some structure.

Tool Count2/5

At 35 tools, the server is overstuffed. The core Pipeworx data and Polymarket analytics surface alone would justify roughly 20 tools, but memory management, subscription lifecycle, llms.txt generation, npm dependency scanning, claim validation, and Art Institute of Chicago lookups are unrelated additions that push the count well beyond a focused scope.

Completeness4/5

Each functional cluster is fairly complete on its own: memory has remember/recall/forget, subscriptions have subscribe/list/recent_alerts/unsubscribe, data lookup has casual, grounded, deep, and validation modes, and prediction markets cover research, edges, arbitrage, fill risk, tracking, and cross-venue spreads. The issue is not missing capabilities but the lack of a single coherent domain.

Resources