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by io.github.pipeworx-io
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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.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "window": "7d"
    },
    "name": "pipeworx_trending"
  }
}

Input 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.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "window": "7d"
+  }
+]
  2. Added

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses that the data is self-aggregating, derived from CF analytics-engine, contains no PII, and only exposes (pack, tool, count). It also states caching behavior (5min-1h depending on window), which is useful operational context not present in the 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 front-loaded with the core result set, then uses a compact numbered list for use cases. Every remaining sentence adds substantive value — data source, privacy, and caching — with no filler or repetition beyond the minor window mention.

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 one-parameter read-only tool, the description fully covers what the agent needs: what it returns, what windows are available, how the data is derived, and that it is privacy-safe. The absence of an output schema is adequately compensated by naming the return components (top tools, top packs, total call volume).

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

Parameters3/5

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

Schema coverage for the single optional window parameter is 100%, including the enum values and an explanatory description that already notes the default and the hot-vs-steady-state tradeoff. The description reiterates the window options but does not add meaning beyond what the schema already provides, so the baseline score of 3 applies.

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 opens with a clear purpose — what other AI agents are calling on Pipeworx right now — and specifies the exact deliverables: top tools, top packs, and total call volume over a window. This is a specific resource plus a concrete return set, making it easy to distinguish from sibling tools like discover_tools or recent_alerts.

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 explicit use cases: discovering hot data sources, confirming a canonical tool, and aligning with aggregate agent behavior. It does not explicitly say when not to use it or name an alternative, but the 'Useful for' list gives clear contextual guidance for choosing this tool.

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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