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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.

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 indicate read-only, idempotent, non-destructive behavior. The description adds valuable details: data source (CF analytics-engine), absence of PII, caching behavior (5min-1h), and self-aggregating nature. No contradiction with annotations.

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 (three short paragraphs) with front-loaded purpose, structured use cases, and brief technical notes. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately explains return content (top tools, packs, call volume). It could optionally detail the output format, but the tool's simplicity makes this sufficient. Context signals (1 param, no output schema) support this score.

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 provides full coverage (100%) with an enum for 'window'. The description adds semantics by explaining that shorter windows surface hot trends while longer windows show steady-state demand, aiding selection beyond the enum values.

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 begins with a clear verb phrase 'What other AI agents are calling on Pipeworx right now' and specifies outputs (top tools, top packs, total call volume) and time windows. It distinguishes itself from siblings like 'discover_tools' and 'ask_pipeworx' by focusing on aggregate usage trends.

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?

Three explicit use cases are listed (discovering hot data sources, confirming canonical tool, aligning with agent needs). However, it does not explicitly state when NOT to use the tool or compare directly to siblings; the guidance is still clear and actionable.

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

ask_pipeworx and ask_pipeworx_beta are explicitly described as functionally identical right now, making them near-duplicates. ai_visibility_check and scan_competitor_ai_presence also heavily overlap, and deep_research vs ask_pipeworx requires careful reading to know which to pick.

Naming Consistency4/5

Most tools follow a clear snake_case verb_noun pattern (list_templates, create_*, validate_claim). However, a few bare verbs break the pattern: compose, forget, recall, and remember.

Tool Count1/5

34 tools is already heavy, but the server is named 'Gitignore' and only 3 of 34 tools relate to .gitignore templates. The other 31 tools form a completely unrelated data platform, making the count an extreme mismatch for the apparent scope.

Completeness3/5

As a data platform, the surface is broad but has notable gaps: citations return pipeworx:// URIs yet no fetch/read-by-URI tool exists, and subscriptions support subscribe/unsubscribe/list but not update. For the gitignore name, only basic template list/get/compose is present, with the rest irrelevant to the stated purpose.