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Data Brussels

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 readOnlyHint and openWorldHint annotations, the description adds substantial behavioral context: the two routing paths, the possible verdicts, and the crucial distinction between could_not_verify (check did not happen) and unsupported (no source coverage). This goes far beyond the annotations and clarifies failure semantics.

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 front-loaded with trigger phrases and use cases, and every sentence adds value: routing, return shape, caller warning, and efficiency benefit. The structure moves from what → when → outputs → pitfalls, which is easy to scan.

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 compensates by enumerating verdicts, the returned value with citation, reasoning, and the verification_error object. It also explains both pipeline paths and the unsupported case, making the behavior complete for a caller.

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?

The schema already documents both parameters at 100% coverage, but the description adds meaning beyond it: tolerance_pct defaults to implied wording capped at 5, and setting it to 1–2 is recommended for hallucination detection. The claim parameter is also illustrated with concrete examples.

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 natural-language trigger phrases and explicitly states the tool performs natural-language claim verification against authoritative sources. It distinguishes the structured SEC/XBRL path for company-financial claims from the grounded pipeline for all other factual claims, making the purpose specific and distinct from siblings.

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?

It clearly says 'Use whenever the agent needs to check whether something a user said is factually correct' and gives trigger examples. It does not explicitly name alternative sibling tools or when-not-to-use cases, but the context is unambiguous and includes a note that it replaces multiple sequential calls.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes, with clear descriptions differentiating similar ones like ask_pipeworx and ask_pipeworx_grounded. However, some overlap exists between deep_research and ask_pipeworx, though descriptions provide guidance.

Naming Consistency2/5

Tool names are inconsistent, mixing snake_case (ai_visibility_check), multi-word phrases (scan_competitor_ai_presence), and simple verbs (query, recall). No uniform pattern like verb_noun convention.

Tool Count3/5

33 tools is on the high side, but the server covers a broad domain (data querying, prediction markets, subscriptions). It feels slightly heavy but still manageable; borderline between reasonable and excessive.

Completeness3/5

The Pipeworx and prediction market tools are comprehensive, but Brussels Open Data is underrepresented with only three tools (query, dataset_info, search_datasets). Missing update/delete operations for Brussels data, though likely read-only.

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