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Setlist Fm

by io.github.pipeworx-io

Deep Research

deep_research
Read-onlyIdempotent

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1499 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,738 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a hop field and a citation_uri — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoHow many facets to research in parallel: quick=3 (single hop), standard=3 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=6 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan).
questionYesThe research question, in natural language. Broad/multi-part is fine — decomposition is the point.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare read-only/idempotent behavior, and the description adds rich runtime context: account and paid-depth requirements, parallel decomposition, findings packet with citations and gaps[], contradiction scans, hop fields, and expected latency. No contradiction with 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 long, but it front-loads the most critical operational warnings (account required, alternative tool, cost) before diving into detail. Every sentence carries substantive information, though a single dense paragraph is harder to scan than a structured layout would be.

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?

With no output schema, the description fully specifies the return packet shape: verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], and hop. It also covers auth, pricing, and timing, so an agent has nearly everything needed to invoke and interpret the tool correctly.

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 description coverage is 100%, so the baseline is 3, but the description adds meaning to depth by noting which values require a paid plan and what each hop level does. It also reinforces that question can be broad and multi-part, which is useful beyond the raw 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?

States a specific verb and resource: performs grounded multi-source research across Pipeworx's 1497 structured data sources in one call. It explicitly contrasts this with open-web search and names ask_pipeworx as the sibling for single lookups, making the tool clearly distinguishable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use this tool ('Best for broad/multi-part questions over structured data') and when not to, directing single lookups and current-news topics to ask_pipeworx. Also covers account/tier prerequisites and the signed-out fallback, leaving no ambiguity about alternatives.

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

A3.6/5.0
Disambiguation2/5

The tool set mixes two completely different domains: setlist.fm (12 tools) and Pipeworx data services (30+ tools). An agent cannot easily distinguish which tools belong to the server's primary purpose, leading to confusion and misselection.

Naming Consistency2/5

Setlist.fm tools use consistent verb_noun patterns (artist, artist_search, artist_setlists), but the majority of tools follow no unified convention: some use snake_case (ai_visibility_check), others use mixed case (ask_pipeworx), creating an inconsistent naming landscape.

Tool Count2/5

43 tools is excessive for a setlist.fm API. Only about 12 are relevant; the remaining 31 are unrelated and bloat the tool surface, making it hard to navigate and maintain.

Completeness4/5

For the setlist.fm domain, the tools cover search, retrieval, and user data comprehensively (artists, setlists, venues, cities, countries, users). Minor gaps exist (e.g., no update/delete operations), but core workflows are supported.

Resources