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The Guardian

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 annotations (read-only, open-world, idempotent), the description reveals the verdict taxonomy, the meaning of could_not_verify versus unsupported, the two routing paths, and the inclusion of pipeworx:// citations. This is substantial added behavioral context.

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 longer than average, but each part earns its place: examples, use-case, path logic, return values, and error semantics. It is well-structured and front-loaded with the core purpose and trigger phrases.

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 no output schema, the description fully explains what the tool returns (verdicts, actual value, citation, reasoning) and clarifies the nuanced difference between could_not_verify and unsupported. It is complete and self-sufficient for an agent to invoke correctly.

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?

Input schema covers both parameters with descriptions. The tool description adds examples for 'claim' but does not materially expand parameter meaning beyond the schema. With 100% schema coverage, baseline 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?

Clearly states the tool performs natural-language claim verification against authoritative sources, with a specific verb ('verify') and resource ('claim'). Distinguishes itself from generic search or ask tools by focusing on fact-checking and listing direct user-phrase triggers.

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?

Explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and differentiates the financial-claims fast path from the general grounded pipeline. Lacks explicit alternatives/exclusions naming sibling tools, but the context is strong enough for correct selection.

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

B3.4/5.0
Disambiguation2/5

Many tools have overlapping purposes and similar names, especially the Pipeworx query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and the Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, etc.). The Guardian-specific tools are distinct but are outnumbered by these confusing clusters, making it hard for an agent to reliably select the right tool.

Naming Consistency1/5

Tool names follow no consistent pattern: some are snake_case (ai_visibility_check, ask_pipeworx), some are verbs without objects (item, tags, forget, recall), and some are inconsistent in style (compare_entities vs. bet_research). There is no unifying naming convention across the set.

Tool Count2/5

With 36 tools, the server is bloated for its implied purpose ('The Guardian' suggests a focused news outlet). Many tools belong to unrelated domains (Pipeworx data platform, Polymarket prediction markets), making the count feel excessive and unfocused. A news-specific server should have far fewer tools.

Completeness1/5

The tool set lacks a coherent domain. The Guardian news tools are complete (search, item, sections, etc.), but the massive inclusion of Pipeworx and Polymarket tools creates dead ends and gaps (e.g., no direct tool to list all prediction markets or search patents). The surface feels like a random collection rather than a designed whole.

Resources