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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. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare read-only and idempotent behavior, and the description adds non-obvious context: the data is self-aggregating from CF analytics-engine, contains no PII, and is cached for 5 minutes to 1 hour depending on window. This goes beyond the annotations and enriches the agent's mental model.

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?

The description is well-structured: a clear lead sentence, a list of use cases, and a final line about data source/caching. It is slightly longer than necessary, but every sentence adds relevant information and there is no fluff.

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 tool with one optional parameter and rich annotations, the description is fully complete: it specifies output contents (top tools, top packs, volume, and (pack, tool, count) tuples), gives concrete use cases, explains caching behavior, and clarifies privacy/aggregation. An output schema is unnecessary here.

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 covers the window parameter fully with enum values and descriptions for hot vs steady-state meaning (100% coverage). The description adds a useful extra detail that the cache TTL depends on the chosen window, providing additional semantic value 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 immediately states what the tool returns ('top tools, top packs, and total call volume'), uses a specific verb ('returns'), and frames the resource as 'what other AI agents are calling on Pipeworx right now.' This clearly distinguishes it from siblings like discover_tools by emphasizing trending aggregate usage.

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 lists three explicit use cases (discovering hot data sources, confirming canonical tools, checking alignment), making the 'when to use' context clear. However, it does not mention when not to use it or name alternative sibling tools, so it stops short of a 5.

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

The ask_pipeworx family (ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded / deep_research) has significant boundary blurring—ask_pipeworx_beta explicitly 'currently matches ask_pipeworx exactly'—and the six prediction-market tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) have heavily overlapping edge-detection purposes. Only the archive, memory, and subscription families are cleanly delineated.

Naming Consistency3/5

There are consistent family prefixes (ask_*, polymarket_*, pipeworx_*) and clean pluralized lists (list_files, list_subscriptions), but the full set mixes bare verbs (remember, recall, forget, search), nouns (entity_profile), and varying patterns (bet_research vs compare_entities, search vs search_within vs recent_changes). Readable in clusters, but no single naming convention binds the set.

Tool Count2/5

35 tools is heavy, and the count is fattened by five distinct product areas—data routing, prediction markets, archive.org access, memory, and subscriptions—that have little to do with each other or with the server name 'archive'. It sits in the 25+ heavy zone even before honoring the mismatch between its name and the sprawl of its purpose.

Completeness3/5

Individual subdomains are well-covered: the memory trio (remember/recall/forget), subscription lifecycle (subscribe/unsubscribe/list_subscriptions/recent_alerts), and archive lineup (search/get_metadata/list_files/wayback_check) are each complete, and extra machinery like pipeworx_feedback and recent_changes shows domain care. But the unifying domain is incoherent—a server named 'archive' that's also a universal data router and prediction-market toolkit—and the scope ends up both bloated and still full of gaps for any one of the intended users (e.g. no archive-item upload, no prediction-market portfolio management).

Resources