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Theaudiodb

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.

TDQS

A4.7/5.0
Behavior5/5

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

Despite having annotations (readOnly, idempotent, openWorld), the description adds crucial behavioral nuance: it explains the distinction between 'could_not_verify' (an error carrying verification_error) and 'unsupported' (no source exists), and warns callers not to treat could_not_verify as evidence. It also discloses the internal routing logic between SEC EDGAR and the grounded pipeline. 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.

Conciseness5/5

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

The description is long but every sentence carries weight—from trigger phrases to the verdict list to the critical caller note. It is front-loaded with usage context and structured with an 'IMPORTANT' callout. No fluff or repetition.

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?

Despite no output schema, the description fully explains return values (verdicts), result contents (actual value, citation, reasoning), and error semantics. It also addresses edge cases like 'unsupported' and 'could_not_verify', making it complete for a verification tool.

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 the description doesn't need to re-explain parameters, but it adds practical guidance: tolerance_pct overrides wording-implied tolerance and suggests setting 1–2 for hallucination detection. It also provides concrete claim examples for the parameter 'claim'. This adds value over 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 clearly specifies the verb 'validate' and the resource 'claim', with explicit natural-language trigger phrases like 'fact check' and 'verify the claim that...'. It also distinguishes itself from siblings by stating it replaces 4–6 sequential calls, making its purpose unique and unambiguous.

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 an explicit when-to-use instruction: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also clarifies the behavior for different claim types but does not explicitly name alternative tools or exclusions, so it stops short of full when/not guidance.

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
Disambiguation2/5

Multiple tool families blur together: ask_pipeworx, ask_pipeworx_beta (currently identical by admission), ask_pipeworx_grounded, and deep_research all route to the same 5,721 tools, and the six polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) heavily overlap on prediction-market analysis. The descriptions are verbose but an agent would struggle to reliably pick the right one without reading thousands of words.

Naming Consistency3/5

All names are snake_case, but conventions vary: verb_noun (get_artist, search_album, list_subscriptions), noun-first (polymarket_edges, entity_profile, recent_alerts), bare verbs (remember, forget, recall, subscribe), and vendor prefixes (pipeworx_*, polymarket_*). More importantly, the server is named Theaudiodb yet almost none of the tool names reflect music, making the naming misleading about the server's actual scope.

Tool Count2/5

35 tools is over the threshold for a well-scoped server, and the sprawl is severe: 4 music tools, roughly 20 data-research tools, 6 prediction-market tools, memory utilities, subscription management, npm scanning, and AI-visibility checks. This is not one coherent server but several servers' tool sets bolted together, with no unifying purpose that justifies the count.

Completeness2/5

For a server named Theaudiodb, coverage is thin: search_artist, search_album, get_artist, and get_album_tracks exist, but there is no search_track, no get_album metadata by ID (only its tracks), no trending/browse-by-genre, and get_artist requires an ID only obtainable by searching first. Meanwhile the 31 non-music tools suggest the real domain is actually Pipeworx data research, making the overall surface feel like an incoherent mix where neither domain is fully covered.

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