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Last Fm

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

A4.8/5.0
Behavior5/5

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

The description goes well beyond the readOnly/openWorld/idempotent annotations by explaining the meaning of each verdict, especially the critical distinction between 'could_not_verify' (did not happen, with error details) and 'unsupported' (no source). It also discloses the use of citations and the overall evidence pipeline, which is valuable behavioral context not present in 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 dense but every sentence earns its place. It front-loads the trigger phrases, then logically explains the two processing paths, the return structure, and the critical caveats for callers. No unnecessary fluff; the length is justified by 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?

Given the tool's complexity, no output schema, and no sibling differentiation, this description is remarkably complete. It explains the return values, edge cases, error semantics, and the fact that it replaces multiple sequential calls. It leaves no ambiguity about what the agent will receive or how to interpret the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already provides 100% coverage of both parameters, and the description adds substantial extra semantics: it explains how tolerance_pct overrides the claim's implied tolerance, gives a range (0.5–50), recommends 1–2 for hallucination detection, and states the default behavior. The claim parameter is clarified with examples and the overall invocation pattern, exceeding what the schema provides.

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/fact-checking, with specific verb phrases like 'fact check' and 'verify the claim that…'. It distinguishes the tool from siblings by emphasizing its automatic routing and single-call nature, which is not replicated in other tool descriptions.

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 explicitly states when to use the tool ('Use whenever the agent needs to check whether something a user said is factually correct') and describes the internal dichotomy between company-financial and other claims. However, it does not explicitly name alternatives or state when not to use this tool versus sibling tools like compare_entities or deep_research, so it falls just short of full explicitness.

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
Disambiguation2/5

Many tools have overlapping purposes, especially the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the Polymarket cluster (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread). Additionally, discover_tools and suggest_questions both serve as discovery/onboarding tools, and ai_visibility_check vs scan_competitor_ai_presence are closely related. While descriptions are detailed, the boundaries between these tools are unclear, causing potential misselection.

Naming Consistency4/5

All tool names use lowercase snake_case with no camelCase or mixed styles. The naming follows a mostly consistent verb_noun or data_subject pattern (e.g., ask_pipeworx, list_subscriptions, validate_claim, artist_info, recent_alerts). Minor deviations exist, such as recent_alerts and user_top_tracks not beginning with a verb, but the overall pattern is predictable and readable.

Tool Count2/5

40 tools is far too many for a server nominally about Last.fm; only 9 tools actually relate to its stated purpose. The remaining 31 tools cover unrelated domains (Pipeworx data routing, Polymarket betting, memory, subscriptions, etc.), making the set bloated and unfocused. Many tools are near-duplicates (ask_pipeworx, _beta, _grounded; four different polymarket_* analysis tools), inflating the count without adding distinct value.

Completeness2/5

Even within the Last.fm domain, the tool surface is incomplete: there is no artist search, album search, user profile info, recent scrobbles, loved tracks, or music recommendations. The unrelated Pipeworx/Polymarket tools, while individually comprehensive for their own domains, do not compensate for the lack of core Last.fm functionality given the server's stated purpose. The overall set is a fragmented mixture that leaves significant gaps for what the server name promises.

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