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Data Nola

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?

Annotations already declare readOnly/openWorld/idempotent, but the description adds rich context: the SEC EDGAR fast path for company-financial claims, fallback to grounded pipeline for any other claim, and the exact meaning of 'could_not_verify' vs 'unsupported' with a verification_error object. It warns callers not to treat could_not_verify as evidence for/against the claim. This is far beyond annotation coverage and has no contradiction.

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 front-loaded with trigger phrases, and every sentence earns its place. Despite being long, it is well-structured: what it does, when to use, routing rules, return payload, and critical caller caveats. No redundancy or filler.

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?

With no output schema, the description fully covers return values (verdict types, actual value, citation, reasoning) and error semantics. It also explains the two execution paths and the tool's role as a replacement for 4–6 sequential calls. The description leaves no significant questions unanswered for the agent.

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?

Schema coverage is 100%, so baseline is 3, but the description adds meaningful semantics: it explains tolerance_pct overrides the wording-implied tolerance, gives concrete example use (1–2 for hallucination detection), and provides example claim formats for the claim parameter. This elevates the parameter understanding well beyond schema 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, lists trigger phrases, and distinguishes it from sequential multi-step approaches. It names a specific verb (validate/verify), resource (authoritative sources), and scope (any factual claim, with a fast path for company-financial claims).

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 says 'Use whenever the agent needs to check whether something a user said is factually correct' and explains the two routing paths. It does not explicitly name alternative sibling tools but clarifies this replaces 4–6 sequential calls, implying the alternative is manual decomposition. There are no exclusions or when-not-to-use, but the guidance is clear enough.

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

Several tool boundaries blur: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve overlapping query/research entry points, and ask_pipeworx_beta is currently identical to ask_pipeworx by the server's own description. entity_profile, recent_changes, and compare_entities also cover overlapping company-investigation territory, forcing agents to parse long descriptions to avoid mis-selection.

Naming Consistency4/5

Names are uniformly snake_case and mostly follow a predictable verb-object or domain-prefix pattern (ask_pipeworx_*, polymarket_*, nola_*, subscribe/unsubscribe). Minor deviations like nola_datasets, polymarket_edges, and ai_visibility_check are noun-first rather than verb-first, but the overall convention is still readable and coherent.

Tool Count2/5

34 tools is well above the 15-25 heavy range and includes multiple near-duplicate query modes, four closely related Polymarket analysis tools, and memory/subscription utilities layered on top of the core data-access surface. While the server appears to be a broad data platform, the count is bloated for an agent to navigate efficiently.

Completeness4/5

The tool surface is remarkably broad: discovery, single-answer lookup, grounded verification, deep research, entity resolution, entity profiles, comparisons, claim validation, NOLA querying, prediction-market analytics, memory, subscriptions, and feedback are all covered with few obvious dead ends. The main gap is that the NOLA-specific surface is thin relative to the server name, though nola_query plus nola_datasets provides a flexible escape hatch.

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