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Glama

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
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds caching latency (5min-1h) and source derivation (CF analytics-engine, no PII), which provides useful behavioral context beyond 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 a single, well-organized paragraph. It front-loads the primary function, then provides bullet-like use cases. Every sentence adds value with no 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 read-only trending tool with one optional parameter, the description fully covers purpose, return content (top tools, packs, volume), source, caching, and privacy. No output schema is needed as return format is clearly described.

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?

The single parameter 'window' has 100% schema coverage with descriptive enum values. The description adds functional meaning: 'Shorter windows surface what's hot right now; longer windows show steady-state demand', aiding correct selection.

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 uses specific verbs ('Returns') and clearly states the resources (top tools, top packs, total call volume) and time window options. It distinguishes itself from siblings like discover_tools and entity_profile by focusing on aggregate trending data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly lists three concrete use cases (discovering hot data sources, confirming canonical tools, checking alignment). Implicitly excludes individual lookups handled by siblings. Also mentions caching behavior, aiding appropriate usage.

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

A3.5/5.0
Disambiguation2/5

The server mixes Medicare-specific tools with many unrelated general-purpose tools (e.g., bet_research, polymarket_arbitrage, remember), and there are multiple similar ask_pipeworx variants. This makes it difficult for an agent to distinguish which tool is appropriate for a given task without confusion.

Naming Consistency2/5

Tool names follow no consistent pattern. Some use a medicare_ prefix with underscores, others use generic verbs like forget, recall, or compound names like ask_pipeworx, deep_research. There is no uniform verb_noun or noun_verb structure.

Tool Count2/5

57 tools is excessive for a server ostensibly focused on 'Medicare Coverage'. Many tools (e.g., bet_research, polymarket_edge_tracker, scan_dependency) are unrelated to Medicare and should be in separate servers, inflating the count and diluting focus.

Completeness4/5

The Medicare-specific tools cover a broad range: NCDs, LCDs, NCAs, enrollment, DME, Part D, hospital, outpatient, post-acute, and provider data. Minor gaps include Medicare Advantage (Part C) and Medicare Supplement, but the coverage is largely comprehensive.