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Newsdata

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

A4.4/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent hints, the description adds vital behavioral nuance: 'could_not_verify means the check did not happen... it is NOT evidence for or against the claim,' and distinguishes unsupported. It also discloses the internal routing logic and that it 'Replaces 4–6 sequential calls,' giving callers confidence about safety and semantics.

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 well-structured, using trigger phrases, a clear use-case statement, routing details, and an important caller warning. Each sentence carries substantive information with no filler, so 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?

There is no output schema, so the description must explain return values—and it does: verdicts, actual value with citation, reasoning, and the special meanings of could_not_verify and unsupported. The routing logic and purpose are fully covered, making the description self-sufficient for correct invocation.

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?

The input schema already provides complete descriptions for both parameters (claim and tolerance_pct), including defaults and override behavior. The description adds little beyond what the schema says, so the baseline of 3 is appropriate.

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 opens with concrete trigger phrases ('Is it true that…', 'fact check') and then defines the tool as 'natural-language claim verification against authoritative sources.' It clearly differentiates from sibling tools by focusing on verifying factual claims, and even specifies a structured 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 states when to use it: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains routing (SEC EDGAR for financial claims, grounded pipeline otherwise), though it does not name sibling tools as alternatives or provide explicit exclusion criteria.

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

A4/5.0
Disambiguation3/5

Most tools have clearly distinct roles, and the extensive descriptions help differentiate intent, but the ask_pipeworx family—especially ask_pipeworx_beta, which is currently identical to ask_pipeworx—creates real ambiguity. Overlapping entry points like ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim could also cause misselection without careful reading.

Naming Consistency4/5

All 34 tools use consistent snake_case, and clear verb-led or noun-prefixed patterns emerge across families like ask_pipeworx*, polymarket_*, and remember/recall/forget. Minor deviations such as ai_visibility_check and recent_changes being noun phrases rather than verb_noun constructions prevent a perfect score.

Tool Count2/5

34 tools is well above the 25-tool threshold for over-scoping, making the set heavy for an agent to navigate. While the server spans many domains, several tools like generate_llms_txt, scan_dependency, and ai_visibility_check feel tangential to the core news/research purpose and would be better split into separate servers.

Completeness4/5

The core news/research domain is well covered: lookup, grounded verification, deep multi-source research, entity profiles, comparisons, subscriptions, and alert feeds are all present with no obvious dead ends. Minor gaps exist—such as no direct full-text article retrieval or a dedicated free-text news search beyond latest_news filters—but agents can work around them.

Resources