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Discogs

by io.github.pipeworx-io

Validate Claim

validate_claim
Read-onlyIdempotent

"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesNatural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
tolerance_pctNoMax percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / properties / tolerance_pct
      Added value: +{
+  "description": "Max percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.",
+  "type": "number"
+}
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive. The description adds substantial behavioral depth: the structured SEC EDGAR fast path vs grounded fallback, the exact list of verdicts, the meaning of 'could_not_verify' (including the verification_error structure and the explicit instruction not to treat it as evidence), and the distinction between 'unsupported' and 'could_not_verify'. This goes well beyond the 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 longer than typical, but every sentence earns its place: it front-loads the core purpose, then provides critical usage details and return-value semantics. The extended list of query phrasings is a bit redundant but helps ground the natural-language scope. It is well-structured and not bloated with boilerplate.

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 there is no output schema, the description carries the full burden of explaining return values. It does this comprehensively: verdicts, grounded/structured actual values, pipeworx:// citations, reasoning, and the error semantics for could_not_verify. It also covers the two major claim categories, making the tool's operational envelope clear. This is complete for an agent to invoke it correctly.

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%, so the baseline is 3. The description does not add parameter-specific semantics beyond what the schema already provides for 'claim' and 'tolerance_pct'. It gives rich examples of claim inputs, but those are illustrative of the concept, not additional parameter documentation. The tolerance_pct behavior is fully in the schema, and the description only implies percent-delta math without connecting it to the parameter.

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's purpose with specific verbs: 'natural-language claim verification against authoritative sources', and provides numerous example phrasings like 'fact check' and 'verify the claim that…'. It also distinguishes itself from siblings by specifying the two verification pathways (SEC EDGAR for company-financial, grounded pipeline for anything else), making it unambiguous.

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 states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It further explains the alternative/replacement nature by noting it 'Replaces 4–6 sequential calls…', giving clear context for when this tool is preferred over a multi-step approach. Conditions for both financial and non-financial claims are laid out.

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
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.

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