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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

A4.6/5.0
Behavior5/5

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

The description significantly expands beyond annotations by explaining the dual-pipeline behavior, the return verdict list, and crucially the semantics of 'could_not_verify' (check did not happen, not evidence) and 'unsupported' (no source covers it). This is high-value context that annotations alone (readOnly, idempotent, openWorld) do not convey.

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 appropriately front-loaded with trigger phrases and core purpose. Every section add necessary context: trigger phrases, pipeline routing, return verdicts, and caller warnings. The 'Replaces 4–6 sequential calls' line is somewhat promotional but informative about efficiency. Slightly verbose, but no 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?

Given the tool's complexity (two processing paths, multiple verdict types, special error semantics) and no output schema, the description does an excellent job covering return values and their meanings. It also explains the fallback behavior for any non-financial claim. An agent has enough information to use this tool correctly and interpret results.

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?

Schema coverage is 100%, so the baseline is 3. The description adds value for tolerance_pct by explaining it overrides the claim's implied tolerance and recommending 1-2 for hallucination detection. It also provides realistic examples for the claim parameter, going beyond the schema's generic 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 specifies a clear verb+resource: 'natural-language claim verification against authoritative sources.' It includes concrete trigger phrases and distinguishes from sibling tools by focusing on fact-checking user claims, with two clearly delineated paths (structured SEC/XBRL vs grounded pipeline).

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 explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct.' It also clarifies when to use the structured financial path vs the generic grounded path. However, it does not explicitly name alternative sibling tools or state when not to use it, so it falls short of a 5.

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

Multiple tools have overlapping purposes (e.g., three variants of ask_pipeworx, several polymarket tools, and multiple discovery/entity tools). Despite detailed descriptions, the similiar functionalities create confusion for an agent selecting among them.

Naming Consistency3/5

Tool names are consistently in snake_case but the verb/noun pattern is mixed: some start with verbs (ask_, list_, remember_), others with nouns (entity_profile, polymarket_edges). This inconsistency reduces predictability.

Tool Count2/5

35 tools is high for a single server, especially when the scope spans two disparate domains (HDX humanitarian data and Pipeworx data platform). Many tools could be logically split into separate, more focused servers.

Completeness3/5

The tool set covers a wide range of functionality (data retrieval, comparison, monitoring, memory) but has notable gaps: no direct data download tool for HDX resources, no account management, and no exploration of Pipeworx packs beyond discover_tools.

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