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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

A4.4/5.0
Behavior4/5

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

Annotations already mark it read-only/idempotent. Description adds value: data source (CF analytics), no PII, caching behavior (5min-1h). No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

Five sentences front-load core function and use cases. Efficient but not minimal; every sentence adds value.

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 single optional parameter, rich annotations, and no output schema, description covers purpose, use cases, data derivation, privacy, and caching 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 covers parameter fully; description adds semantic guidance on window choice for hot vs steady-state demand.

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 it returns top tools, top packs, and call volume over windows, distinguishing it from siblings like 'discover_tools' by focusing on what other agents are calling.

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 use cases and explains window selection (short for hot, long for steady-state). No explicit when-not-to-use, but context is clear.

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

Multiple tools have overlapping boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ask_pipeworx_grounded and deep_research heavily overlap with them, and entity_profile, compare_entities, recent_changes, and validate_claim all circle the same company-data space. The prediction-market cluster (polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) also requires careful reading to distinguish. The descriptions help, but the set relies on them to disambiguate near-duplicates.

Naming Consistency4/5

Nearly all tools follow a consistent lowercase snake_case style, whether verb_noun (search_complexes, validate_claim, unsubscribe), noun_verb (entity_profile, recent_changes), or brand-like (ask_pipeworx, polymarket_edges). There is no camelCase mixing or chaotic verb style. Minor inconsistency exists between imperative verbs (remember, forget, subscribe) and noun-style names, but the overall pattern is readable and predictable.

Tool Count2/5

33 tools is well above the 25-tool 'heavy' threshold, and many are meta-tools (discover_tools, suggest_questions, pipeworx_feedback, pipeworx_trending, remember/recall/forget) that pad the surface. The server is named 'Complex Portal', yet only two tools serve that purpose—the rest belong to a broad data platform. The count feels inflated and misaligned with the server's stated identity.

Completeness3/5

For the broad Pipeworx data platform the surface is fairly complete: universal querying, grounded answers, deep research, entity profiles, comparisons, claim validation, subscriptions, memory, and feedback. For the Complex Portal domain named by the server, coverage is thin—just search and fetch-by-accession, with no browsing, species filtering, or cross-reference tools. The core workflow works, but the namesake domain is under-served relative to the rest of the set.