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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, non-destructive), the description discloses data source (CF analytics-engine), privacy (no PII), caching behavior (5min-1h), and the self-aggregating nature. This adds significant behavioral insight.

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 concise, structured with a clear main sentence, bulleted use cases, and supplementary details. Every sentence adds value without redundancy.

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?

For a simple read-only tool with one optional parameter and no output schema, the description covers purpose, usage context, behavioral details, and parameter semantics. It is complete and leaves no significant gaps.

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

Parameters5/5

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

Schema coverage is 100% and the description adds meaning: it explains the effect of different windows (short vs long) and provides guidance on choosing between them. This goes beyond the schema's enum list.

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, top packs, and call volume over configurable windows, with three explicit use cases. It differentiates from siblings by focusing on trending analytics rather than direct querying or entity resolution.

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 three specific scenarios where the tool is useful (discovering hot data, confirming canonical tools, aligning use cases). It lacks explicit "when not to use" or alternative tool mentions, but the context is sufficiently clear for an agent to decide.

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

A3.6/5.0
Disambiguation2/5

Multiple research entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and company analysis tools (entity_profile, compare_entities, recent_changes, bet_research) overlap heavily; the five polymarket_* tools also require careful reading to distinguish. Descriptions are detailed, but an agent must parse long disambiguation text to avoid mis-selection.

Naming Consistency2/5

Names mix verb-first (list_subscriptions, validate_claim, remember), noun-first (entity_profile, polymarket_arbitrage), product-prefixed (ask_pipeworx, pipeworx_feedback), and brand-prefixed (diffbot_company, diffbot_extract). No consistent verb_noun pattern across the set; polymarket_* and pipeworx_* prefixes are internally consistent but the overall scheme is chaotic.

Tool Count2/5

33 tools is well beyond the 3-15 sweet spot and in the 'too many' range. Many tools are meta-variants (4 ask_pipeworx flavors, 5 polymarket tools, 2 AI-visibility tools) that could be consolidated.

Completeness3/5

The data-research, prediction-market, memory, and subscription subdomains are each fairly complete at a meta level, with few dead ends. Gaps include no subscription update (delete + recreate required), no explicit memory update, and no direct way to execute an arbitrary discovered Pipeworx tool aside from routing through ask_pipeworx.

Resources