Skip to main content
Glama

Entertainment Feeds

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

A4.2/5.0
Behavior4/5

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

The description adds context beyond annotations: caching duration (5min-1h), data source (CF analytics-engine), no PII, and self-aggregating nature. Annotations already cover read-only, idempotent, and non-destructive traits.

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 return value and use cases in bullet points, with no unnecessary words.

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 read-only tool, the description covers caching, data source, privacy, and use cases. Lack of output format details is acceptable given the description's clarity on what is returned.

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?

Schema coverage is 100% and the description repeats the schema's parameter explanation verbatim, adding no new meaning. 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 returns top tools, top packs, and total call volume over a recent window, distinguishing it from siblings like discover_tools by focusing on what other AI 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?

It explicitly lists three specific use cases (discovering hot data sources, confirming canonical tools, aligning use cases) but does not mention when not to use or name alternatives.

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

All Entertainment Feeds toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation3/5

While most tools have distinct purposes, there is notable overlap between the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and deep_research. Similarly, bet_research and polymarket_edges both analyze betting opportunities. These overlapping tools can cause confusion for an agent.

Naming Consistency2/5

Tool names follow a snake_case pattern, but the verbs used are highly varied (ask_, bet_, compare_, deep_, discover_, entity_, fetch_, forget_, generate_, list_, pipeworx_, polymarket_, read_, recall_, recent_, remember_, resolve_, scan_, search_, subscribe_, suggest_, unsubscribe_, validate_). This lack of a consistent verb_noun pattern makes it harder to predict tool names.

Tool Count2/5

34 tools is excessive for an 'Entertainment Feeds' server. The majority of tools are unrelated to entertainment (e.g., SEC filings, FDA drugs, FRED data, Polymarket betting). Many of these should belong to a separate 'data-access' server, making the scope unwieldy and unfocused.

Completeness2/5

For an entertainment feeds server, the set is incomplete. It can list and read curated feeds and fetch arbitrary RSS, but lacks tools for managing subscriptions to those feeds, searching across feeds, or creating feeds. The inclusion of many non-entertainment tools doesn't compensate for these gaps.

Resources