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microlink

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. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds valuable context: data derived from a specific analytics engine, no PII, only (pack, tool, count), and caching behavior varying by window (5min-1h). This goes beyond the annotations and gives the agent insight into data freshness and privacy.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is somewhat long but front-loaded with the core function, followed by a clearly numbered list of use cases and a final sentence on data origin and caching. Each part adds value, though the list could be seen as slightly verbose. Overall it is well-structured and not wasteful.

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 tool with rich annotations and no output schema, the description is quite complete. It explains what data is returned, the time windows, use cases, caching, and privacy characteristics. No obvious gaps remain for an agent to invoke it correctly.

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?

The schema already covers the only parameter (window) with a description and enum values. The tool description adds a minor note that caching duration depends on window, but otherwise does not significantly enhance the parameter meaning. With 100% schema coverage, baseline 3 is appropriate.

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's function: returning top tools, top packs, and total call volume over a recent window (24h/7d/30d). It uses a specific verb ('Returns') and resource ('top tools, top packs, total call volume'), and the title 'Pipeworx Trending' aligns well. It distinguishes from siblings like discover_tools by focusing on current agent usage trends.

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 explicit use cases: (1) discovering hot data sources for current events, (2) confirming canonical tool choice, (3) aligning with agent needs. It does not name alternatives or exclusions, but the context is clear enough for an agent to decide when to use this tool. This fits 'clear context, no exclusions'.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed usage guidance, but the several query entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) overlap conceptually and require careful reading to select correctly. Notably, ask_pipeworx_beta currently behaves identically to ask_pipeworx, which could cause confusion.

Naming Consistency3/5

Tool names mix verb-first (remember, resolve_entity) and noun-first (entity_profile, deep_research) patterns, with some using prefixes like 'polymarket_' or 'ask_pipeworx'. While all are snake_case and readable, the lack of a single consistent convention makes the set feel less coherent than it could be.

Tool Count3/5

33 tools is on the high end for a single server, though the broad domain (data retrieval, prediction markets, memory, subscriptions, web scraping) justifies much of the sprawl. Still, the count borders on heavy, and some tools could potentially be consolidated (e.g., the ask_pipeworx variants).

Completeness4/5

The toolset provides comprehensive coverage for its core data querying and analysis domain, with lifecycle coverage for memory and subscriptions. Minor gaps exist, such as no generic 'fetch page content' tool despite having get_metadata and take_screenshot, but these do not undermine the primary functionality.

Resources