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Theaudiodb

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

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. Description adds valuable behavioral context: data is 'self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count)' and 'Cached 5min-1h depending on window.' This explains data sources and freshness 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?

Description is well-structured with a clear lead sentence followed by bullet points summarizing use cases and data details. It is concise but could be slightly tighter; every sentence earns its place.

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

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, the description explains the return value concept (top tools, packs, call volume) and data source. However, it does not describe the output format or structure, which would be helpful for the agent to interpret results. This gap prevents a higher score.

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 only parameter, 'window', has a description in the schema that already explains the options. Schema description coverage is 100%. The description adds caching behavior per window, which is a minor addition. Baseline 3 is appropriate as the schema does the heavy lifting.

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's function: 'Returns the top tools, top packs, and total call volume over a recent window.' It specifies the resource (Pipeworx trending data) and action (returns). The purpose is distinct from sibling tools like discover_tools or suggest_questions, focusing on what is currently popular.

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?

Description provides three explicit use cases (discovering hot data sources, confirming canonical choice, seeing alignment) and explains how window length affects results ('Shorter windows surface what's hot right now; longer windows show steady-state demand.'). However, it does not explicitly mention when not to use this tool or suggest alternatives, such as ask_pipeworx for direct queries.

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

All Theaudiodb toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tool families blur together: ask_pipeworx, ask_pipeworx_beta (currently identical by admission), ask_pipeworx_grounded, and deep_research all route to the same 5,721 tools, and the six polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) heavily overlap on prediction-market analysis. The descriptions are verbose but an agent would struggle to reliably pick the right one without reading thousands of words.

Naming Consistency3/5

All names are snake_case, but conventions vary: verb_noun (get_artist, search_album, list_subscriptions), noun-first (polymarket_edges, entity_profile, recent_alerts), bare verbs (remember, forget, recall, subscribe), and vendor prefixes (pipeworx_*, polymarket_*). More importantly, the server is named Theaudiodb yet almost none of the tool names reflect music, making the naming misleading about the server's actual scope.

Tool Count2/5

35 tools is over the threshold for a well-scoped server, and the sprawl is severe: 4 music tools, roughly 20 data-research tools, 6 prediction-market tools, memory utilities, subscription management, npm scanning, and AI-visibility checks. This is not one coherent server but several servers' tool sets bolted together, with no unifying purpose that justifies the count.

Completeness2/5

For a server named Theaudiodb, coverage is thin: search_artist, search_album, get_artist, and get_album_tracks exist, but there is no search_track, no get_album metadata by ID (only its tracks), no trending/browse-by-genre, and get_artist requires an ID only obtainable by searching first. Meanwhile the 31 non-music tools suggest the real domain is actually Pipeworx data research, making the overall surface feel like an incoherent mix where neither domain is fully covered.

Resources