Skip to main content
Glama

Emsc

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds important context: data is 'derived from CF analytics-engine', 'no PII', and 'cached 5min-1h depending on window'. No contradictions.

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 paragraphs) and well-structured: first sentence states the purpose, next lists use cases, final paragraph adds transparency details. 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?

With one optional parameter and no output schema, the description fully covers what the tool returns (top tools, top packs, call volume), its caching behavior, and data origin. No gaps remain for an agent to use it correctly.

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 coverage is 100% for the single parameter 'window'. The description reinforces its meaning and adds nuance: 'Shorter windows surface what's hot right now; longer windows show 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?

The description clearly states the tool returns 'top tools, top packs, and total call volume' over a configurable window. It explicitly distinguishes itself from siblings like 'discover_tools' by focusing on trending usage data rather than tool discovery.

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 use cases (discovering hot data sources, confirming canonical tools, checking alignment with agent needs). However, it doesn't explicitly state when not to use this tool or name direct alternatives among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Emsc toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0
Disambiguation3/5

Multiple tools overlap: ask_pipeworx and ask_pipeworx_beta are currently identical, and the five prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker) cover adjacent tasks that require careful reading. However, descriptions are unusually explicit about when to prefer each, and non-overlapping domains (memory, subscriptions, earthquakes, npm) are clearly separated.

Naming Consistency4/5

All tool names are snake_case with a mostly verb-first pattern (ask_, compare_, discover_, generate_, list_, scan_, search_, validate_), making the surface predictable. Minor deviations like deep_research, entity_profile, and single-word verbs (remember, recall, forget) break the pattern slightly, but each family is internally consistent.

Tool Count2/5

33 tools exceeds the 25+ threshold for 'too many,' and several could be consolidated — ask_pipeworx_beta is redundant today, and the prediction-market suite could fold into 2-3 tools. The count reflects a genuinely wide data platform with meta-tools (discover_tools, suggest_questions, ask_pipeworx) already covering discovery, so the surface feels heavy for an agent to triage.

Completeness4/5

Core workflows are well covered: querying (ask_pipeworx, grounded, deep_research), research profiles (entity_profile, compare_entities, recent_changes), input resolution (resolve_entity), fact-checking (validate_claim), memory lifecycle, and subscription lifecycle all have complete loops. Minor gaps exist, notably no tool to fetch a pipeworx:// citation URI directly (search_within expects already-fetched text), and some one-off tools like generate_llms_txt and scan_dependency feel bolted on.

Resources