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Serpapi

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.

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false), the description adds valuable behavioral context: the data is derived from CF analytics-engine, no PII, just counts, and caching varies from 5min-1h depending on window. 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 two sentences followed by a bulleted list of use cases. It is front-loaded with what the tool returns, then why to use it, then technical details. 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 simple tool with one optional parameter and no output schema, the description covers purpose, use cases, data source, privacy, and caching. No gaps are apparent.

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 description coverage is 100% for the single parameter 'window', but the description adds meaningful context: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This enhances understanding 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 clearly states the tool returns 'top tools, top packs, and total call volume over a recent window.' It specifies the action ('Returns') and the resource (trending data on Pipeworx), distinguishing it from siblings like discover_tools or ask_pipeworx.

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 specific use cases for when to use the tool, such as discovering hot data sources or confirming a canonical tool. It also provides guidance on window selection ('shorter windows surface what's hot right now; longer windows show steady-state demand'). It does not explicitly state when not to use it, but the use cases are clear.

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
Disambiguation3/5

Many tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, deep_research, and bet_research all querying data but with nuanced differences. The descriptions help but boundaries remain fuzzy, especially between ask_pipeworx and deep_research for broad vs. single lookups. Overall moderate ambiguity.

Naming Consistency2/5

Naming is inconsistent: some tools use snake_case (ask_pipeworx, ai_visibility_check), others use camelCase (serpapi_google_jobs), and patterns vary widely (e.g., pipeworx_feedback vs. compare_entities). Only the serpapi_google_* group follows a consistent pattern.

Tool Count3/5

36 tools is on the high side for a single server, with many meta-tools (discover_tools, suggest_questions) and niche prediction market tools. The scope seems overly broad, covering data lookup, prediction markets, memory, and subscriptions, which could be streamlined to a more focused set.

Completeness3/5

The server covers a wide range of domains (financial, economic, news, drugs, prediction markets, Google services), but lacks direct web search and write/update capabilities. While the coverage is broad, there are notable gaps (e.g., no generic web search, limited tool for modifying data) for a data-focused server.

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