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Data Tours

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

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, not destructive), description discloses self-aggregating signal from CF analytics-engine, no PII, and caching behavior (5min-1h). 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?

Single paragraph, front-loaded with output, bullet-style use cases, and methodology note. Every sentence adds value; no fluff.

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 read-only trending tool with one optional parameter, description covers return fields, caching, data source, and privacy. No output schema needed; complete for agent to use effectively.

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?

Only parameter (window) is fully covered by schema (100% coverage). Description adds meaning: default is 24h, shorter windows surface what's hot, longer show steady-state demand, providing context beyond 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?

Description clearly states the tool returns 'top tools, top packs, and total call volume over a recent window', specifying the resource (Pipeworx trending data) and the verb (returns). It distinguishes from siblings like discover_tools by focusing on what agents are calling.

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 provided (discovering hot data sources, confirming canonical tool, aligning use case). Window usage guidance is given (shorter for hot, longer for steady-state). No explicit when-not-to-use or alternatives, but context is 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

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose. Even closely related tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research are well-differentiated by their use cases and safety guarantees. The multiple polymarket tools each focus on a unique aspect (arbitrage, edge scanning, persistence, fill risk, cross-venue spreads), avoiding ambiguity.

Naming Consistency4/5

All tool names use lowercase with underscores, following a mostly verb_noun or domain_prefix_noun pattern (e.g., ask_pipeworx, entity_profile, resolve_entity). A few names like dataset_info and ai_visibility_check deviate slightly from a strict verb_noun structure, but the overall pattern is predictable and readable.

Tool Count4/5

With 33 tools, the server is on the heavier end of the well-scoped range. However, the count is justified by the breadth of functionality: data queries, prediction markets, entity resolution, memory, monitoring, and more. The tools each serve a specific purpose, and none feel redundant.

Completeness4/5

The tool surface covers a wide range of use cases including data retrieval, comparison, research, monitoring, and memory. Minor gaps exist (e.g., no tool for placing prediction market trades or creating/updating Tours Métropole datasets), but these are likely intentional scope choices. Core workflows are well-supported.

Resources