Skip to main content
Glama

Discogs

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 1506 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 5,767 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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan)."New value: +"How 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)."
  2. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus a contradictions[] scan across findings)."New value: +"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets + a contradictions[] scan across findings), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus the contradictions[] scan)."
  3. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3, standard=5 (default), thorough=8 (paid plans). \"thorough\" also runs a second ITERATIVE hop — a planner inspects the first-pass findings/gaps and chases the most valuable leads or recovers gaps, resolving multi-step questions in one call."New value: +"How many facets to research in parallel: quick=3 (single hop), standard=5 (default; adds a gap-recovery hop that re-angles unanswered facets), thorough=8 (paid; adds a full iterative hop that chases leads + recovers gaps, plus a contradictions[] scan across findings)."
  4. Changed1 schema field changed
    • changedInput schema / properties / depth / description
      Previous value: -"How many facets to research in parallel: quick=3, standard=5 (default), thorough=8 (paid plans)."New value: +"How many facets to research in parallel: quick=3, standard=5 (default), thorough=8 (paid plans). \"thorough\" also runs a second ITERATIVE hop — a planner inspects the first-pass findings/gaps and chases the most valuable leads or recovers gaps, resolving multi-step questions in one call."
  5. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destruction. The description adds substantial behavior beyond annotations: parallel tool routing across 5,767 tools, gaps[] never invented, contradictions[] for standard/thorough, semantic excerpting, citation_uri only when fetchable, hop field, and latency expectations. It falls short of 5 only because it doesn't detail failure modes or rate limits, but the behavioral disclosure is rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is information-dense and front-loaded with the most critical operational caveat (ACCOUNT REQUIRED). However, it is quite long and somewhat sprawling, mixing prerequisites, tool mechanics, output format, latency, and alternatives into a single paragraph without clear structural separation. Every sentence earns its place, but structure could be improved with line breaks or bullet-like separation.

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 tool's complexity (5,767 tools, parallel decomposition, paid tiers, citations, gaps, contradictions, latency), the description covers the essential operational context: auth requirement, free/paid tier restrictions, fallback alternative, input type, output packet components, citation resolvability, and latency bounds. No output schema exists, so the description carries the full burden for return-value expectations and does so thoroughly.

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 meaningful semantics for the depth parameter by explaining what each depth tier actually does (quick=3 single-hop, standard=gap-recovery + contradictions, thorough=paid iterative hop + contradictions). It also frames question as broad/multi-part-friendly. The only small gap is that exact semantics of the depth enum values are partially repeated from the schema, but the description adds behavioral consequences 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 states a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1506 STRUCTURED data sources' in ONE call. It clearly differentiates from open-web search and from siblings like ask_pipeworx and ask_pipeworx_grounded by emphasizing structured data, parallel decomposition, and findings packets with citations.

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?

Explicit when-to-use guidance: 'Best for broad/multi-part questions over structured data' and explicit when-not-to-use: 'For a single lookup use ask_pipeworx instead' and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'. It also names the sign-in prerequisite and alternative for non-signed-in users.

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

All Discogs 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
Disambiguation1/5

There is severe overlap among tools: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants, and deep_research, discover_tools, and suggest_questions all serve discovery/research purposes. The Discogs-specific tools are distinct, but the large number of redundant Pipeworx tools makes it impossible to tell which one to pick.

Naming Consistency2/5

The Discogs tools follow a clean verb_noun pattern (get_artist, get_label, get_master, get_release), but the majority of the set uses inconsistent, domain-specific names (deep_research, generate_llms_txt, polymarket_arbitrage, scan_competitor_ai_presence). No single naming convention is applied across the server.

Tool Count1/5

With 36 tools, the server is massively over-provisioned for a Discogs-focused API. Most tools (e.g., ask_pipeworx, polymarket_edges, SEC lookups) have nothing to do with Discogs and belong to a separate service. The Discogs surface alone could be served by ~6 tools (search, get_artist, get_label, get_master, get_release, search_within), so the count is wildly inappropriate.

Completeness3/5

For the Discogs domain, the core entities (artist, label, master, release) and full-text search are present, plus semantic search inside records. However, there are gaps like user collections, wantlists, marketplace, and discogs-specific filters beyond format/country. The presence of many unrelated tools does not directly hurt domain coverage, but the Discogs surface is not exhaustive.

Resources