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Glama

musicurainz

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. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses critical behavioral nuances: the meaning of each verdict, the distinction between 'could_not_verify' (check did not happen) and 'unsupported' (no source exists), and the presence of verification_error{stage,detail}. It also mentions the internal routing and the 'replaces 4-6 sequential calls' efficiency, which annotations do not cover.

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 tightly packed with essential info: start triggers, routing logic, return values, and error handling. It is front-loaded with example queries. Every sentence earns its place, though it could be slightly trimmed without losing meaning.

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 and lack of an output schema, the description is exceptionally thorough. It explains all possible verdicts, that the response includes a grounded/structured actual value with a citation and reasoning, and clearly distinguishes 'could_not_verify' from 'unsupported' with error semantics. This fully equips the agent to interpret and use results 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 coverage is 100% for both parameters, but the description adds valuable semantics: for tolerance_pct it explains the default is implied by wording with a cap of 5, and suggests setting 1-2 for hallucination detection. It also provides concrete examples for the claim parameter, enhancing the schema's basic descriptions.

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 verifies natural-language factual claims against authoritative sources, using specific trigger phrases like 'is it true that' and 'fact check'. It distinguishes itself from sibling tools by explaining its unique dual-path routing (SEC EDGAR/XBRL for financial claims, grounded pipeline for everything else).

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 says 'Use whenever the agent needs to check whether something a user said is factually correct' and gives examples of user intents. It also clarifies what situations each path handles (company-financial vs. other claims), though it does not explicitly name tools to avoid in favor of this one.

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

A3.8/5.0
Disambiguation3/5

Many tools are crisply separated (memory CRUD, subscription lifecycle, single-entity vs compare vs profile), but several broad entry points overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are very close variants, and discover_tools/suggest_questions/ask_pipeworx all serve discovery/routing. Descriptions help, but an agent can still easily select one of the duplicate or adjacent tools instead of the intended one.

Naming Consistency3/5

All names are readable snake_case and there are coherent families (polymarket_*, ask_pipeworx_*, search_*, get_*), but there is no consistent verb_noun convention: noun-phrase names like recent_alerts and pipeworx_trending coexist with single verbs like remember and forget and domain-prefixed nouns like polymarket_edges. Mixed, but still reasonably navigable.

Tool Count2/5

At 35 tools, the surface is well past the comfortable 3-15 tool scope and even beyond the 16-25 heavy range unless the server has one explicit mega-purpose. The set also sprawls across music lookup, Pipeworx research, prediction markets, npm checks, LLM visibility, memory, and subscriptions, so no single coherent job emerges.

Completeness4/5

For the dominant read-only research workflow, the set is remarkably complete: discover tools, grounded and ungrounded asking, deep research, entity resolution, profiles, recent changes, comparisons, claim verification, search_within, plus full memory and subscription lifecycles. Minor gaps include the shallow music side relative to the rest of the server and the absence of an explicit source catalog.

Resources