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Glama

Hud

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds valuable context: data source (CF analytics-engine), no PII, caching behavior (5min-1h depending on window), and the self-aggregating nature. This goes beyond annotations.

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 concise and front-loaded with the main purpose, followed by use cases and implementation details. It is well-structured without unnecessary words, though it could be slightly more compact.

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?

Given the single optional parameter and no output schema, the description fully covers purpose, usage, behavior, and parameter meaning. An agent can correctly select and invoke this tool without additional context.

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%, so baseline is 3. The description adds meaning to the 'window' parameter by explaining what shorter vs longer windows surface ('Shorter windows surface what's hot right now; longer windows show steady-state demand.'), providing additional guidance beyond the schema.

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 what the tool does: 'Returns the top tools, top packs, and total call volume over a recent window.' It uses specific verbs and resources, and the sibling list shows no direct competitor, making it easy to distinguish.

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, giving clear guidance on when to use the tool. However, it does not explicitly mention when not to use it or how it compares to alternatives, so it is not a 5.

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

All Hud toolsInspect
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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes, but several query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, compare_entities, validate_claim) overlap in functionality, which could confuse an agent. The Polymarket and HUD subgroups are well-separated.

Naming Consistency3/5

Tool names use multiple styles: verb_noun (ask_pipeworx), prefixed groups (hud_*, polymarket_*, pipeworx_*), and standalone verbs (forget, recall). While subgroups are consistent, the overall set lacks a uniform pattern.

Tool Count3/5

With 35 tools, the server offers broad data and analytics capabilities. The count is on the high side but justified by the range of features (HUD, general queries, prediction markets, memory, subscriptions). Some tools are highly specialized.

Completeness4/5

The tool surface covers housing data, multi-source querying, prediction markets, memory, subscriptions, and meta-tools. Minor gaps exist (e.g., deeper user account management), but core workflows are well-supported.

Resources