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Glama

Spotify

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

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds substantial behavioral context: it explains the two execution paths (SEC EDGAR vs grounded pipeline), clarifies the meaning of the 'could_not_verify' versus 'unsupported' verdicts, and warns that 'could_not_verify' must not be treated as evidence. This goes far beyond the annotation hints.

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

Conciseness5/5

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

The description is compact yet information-dense. Every sentence earns its place: examples, purpose, routing logic, return values, and a critical caller warning. The structure with an 'IMPORTANT' section effectively highlights the 'could_not_verify' caveat, and the overall length is appropriate for the tool's complexity.

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?

There is no output schema, so the description carries the full burden of explaining return values. It enumerates the verdicts ('confirmed', 'approximately_correct', 'refuted', 'inconclusive', 'unsupported', 'could_not_verify'), mentions the actual value with citation, and describes reasoning. It also covers edge cases like 'could_not_verify' versus 'unsupported', making it complete for a complex claim-verification tool.

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%, and both 'claim' and 'tolerance_pct' have detailed descriptions in the schema. The tool description adds little beyond the schema — it mentions tolerance_pct in the context of hallucination detection and gives claim examples, but these are already in the schema. The baseline of 3 applies since 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?

The description clearly states the tool's purpose: natural-language claim verification against authoritative sources. It provides a specific verb ('validate'), the resource ('claim'), and examples of user phrasings. It also distinguishes from siblings by noting it replaces multiple sequential calls (NL parsing → entity resolution → data lookup → comparison) and handles both structured SEC EDGAR and grounded pipelines.

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?

The description gives explicit when-to-use guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the routing logic for different claim types, providing clear context. However, it does not explicitly list when-not-to-use or name alternative tools, so it does not fully satisfy the highest bar.

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

B3.1/5.0
Disambiguation1/5

The tool set is a chaotic mix of Spotify music tools and an extensive Pipeworx data querying system, with no clear separation. Tools like 'ask_pipeworx', 'ask_pipeworx_grounded', and 'deep_research' have overlapping querying purposes, while unrelated tools from prediction markets and company research further muddy the boundaries. An agent would struggle to distinguish which tool to use for a given task.

Naming Consistency2/5

Naming conventions are inconsistent: Spotify tools follow a verb_noun pattern (e.g., 'get_album', 'search'), while Pipeworx tools use descriptive phrases (e.g., 'ask_pipeworx', 'bet_research'). Additionally, some tools have vague names like 'process' or 'run' that could apply to anything. The lack of a unified naming scheme makes the set feel disjointed.

Tool Count2/5

With 36 tools, the count is high, but the server name 'Spotify' implies a focused music service. The majority of tools are unrelated to Spotify (e.g., Pipeworx queries, Polymarket bets, dependency scanning), making the tool count feel inflated and inappropriate for the stated domain. A more focused set would be far more coherent.

Completeness1/5

If this is a Spotify server, it is severely incomplete: it lacks playlist management, user actions, and recommendation features beyond top tracks. The inclusion of dozens of unrelated tools (financial data, prediction markets, package analysis) suggests the server has no clear purpose, leaving it neither complete for Spotify nor for any other single domain.

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