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Glama

uselessfacts

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

A4.4/5.0
Behavior5/5

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

The description goes well beyond the annotations by disclosing the data source ('derived from CF analytics-engine'), privacy ('no PII'), and the caching behavior ('Cached 5min-1h'). These traits inform the agent about potential staleness and data provenance, adding real value beyond the readOnlyHint and idempotent hints.

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 concise and front-loaded with the core purpose, then lists use cases, then adds technical details. Every sentence contributes value, and the structure is easy to scan, making it exemplary for a tool description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

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 covers the input, output nature (top tools, top packs, total call volume), and relevant behavioral nuances. It doesn't specify the exact output schema, but given no output schema is provided, the description's outline of return content is sufficient for an agent to understand what to expect.

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 fully describes the single parameter with enum values and a semantic distinction between windows ('Shorter windows surface what's hot right now; longer windows show steady-state demand'). The description only repeats the window enumeration without adding further meaning, so it does not improve on the high schema coverage baseline.

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 a specific verb and resource: 'Returns the top tools, top packs, and total call volume over a recent window.' It distinguishes itself from siblings by framing the tool as an aggregate signal of what other AI agents are calling, which is unique among the listed tools.

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 agent needs. However, it does not explicitly state when not to use the tool or name alternatives, so it stops short of full exclusion guidance.

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 (e.g., ask_pipeworx for general queries, ask_pipeworx_grounded for high-stakes verification, deep_research for multi-faceted research). However, some overlap exists among the ask_* variants and the prediction-market tools (bet_research vs. polymarket_edges vs. polymarket_arbitrage), which could cause misselection without careful reading of the detailed descriptions.

Naming Consistency4/5

Tool names consistently use snake_case and mostly follow the verb_noun pattern (e.g., list_subscriptions, resolve_entity, validate_claim). Minor deviations like random_fact and today_fact (adjective_noun) and pipeworx_feedback (noun_noun) introduce slight inconsistency, but the overall pattern is predictable.

Tool Count3/5

With 33 tools, the count exceeds the typical 3-15 range and even the 16-25 'heavy' threshold. While the server covers an unusually broad domain (data retrieval, prediction markets, memory, subscriptions, AI visibility), several tools could be consolidated (e.g., the six polymarket tools, trivial random_fact/today_fact). The scope partially justifies the count, but it feels over-provisioned.

Completeness4/5

The tool surface is remarkably comprehensive for a data platform: it covers querying, entity resolution, comparison, change feeds, memory persistence, subscription management, validation, and even meta-tool discovery. Minor gaps exist (e.g., no explicit update/delete for external data, but that is not the service's purpose). Overall, no obvious dead ends.

Resources