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Boi Il

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 annotations (readOnlyHint, openWorldHint, idempotentHint), the description reveals the dual-pipeline mechanism, the complete verdict vocabulary, and the critical caveat that 'could_not_verify' means the check failed and is not evidence, while 'unsupported' means no source exists. It also discloses the citation format and tolerance override behavior, adding substantial 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 front-loaded with user phrasing examples and is logically structured. It is longer than a typical description, but the complexity of the tool (two paths, six verdicts, error semantics) justifies the length; every section contributes necessary information without redundancy.

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 thoroughly covers return values: the six verdicts, the actual value with pipeworx:// citation, reasoning, and the distinction between 'could_not_verify' and 'unsupported'. The two processing paths are explained, and integration with the agent's workflow is clear, making the description complete for its complexity.

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?

Schema coverage is 100%, giving a baseline of 3. The description adds some context about tolerance being implied by wording and capped at 5%, and mentions exact percent-delta math, but it does not add significant parameter-level meaning beyond what the schema already provides for both 'claim' and 'tolerance_pct'.

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 identifies the tool as natural-language claim verification, enumerates trigger phrases, and specifies two processing paths (company financials via SEC EDGAR/XBRL, all others via grounded pipeline). It also distinguishes itself from multi-step alternatives by stating it replaces 4–6 sequential calls, making its purpose unambiguous.

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 instructs 'Use whenever the agent needs to check whether something a user said is factually correct,' providing a clear trigger condition. It also differentiates sub-cases (company-financial vs other claims) but does not explicitly name alternative sibling tools, which prevents 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

A4/5.0
Disambiguation3/5

Most tools have highly detailed descriptions that clarify their distinct roles, and the pipeworx/boi/polymarket families are individually distinguishable. However, ask_pipeworx_beta is explicitly described as currently identical to ask_pipeworx (a true duplicate), and the polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) share overlapping purpose and could cause misselection despite their lengthy docs.

Naming Consistency3/5

Many tools follow a clear verb_noun pattern (compare_entities, discover_tools, resolve_entity, validate_claim), but a large subset uses noun-first or prefixed compound names (ai_visibility_check, bet_research, boi_exchange_rate, polymarket_arbitrage). The naming is readable and group-consistent (boi_*, polymarket_*, ask_pipeworx*) but the overall convention is mixed rather than uniform.

Tool Count2/5

At 34 tools, the set exceeds the 25+ threshold for 'too many' and spans many unrelated domains (data lookup, prediction markets, memory, subscriptions, AI visibility, llms.txt generation, feedback). The broad scope explains the count, but many tools feel like add-on utilities rather than a tightly scoped server, making the surface feel bloated.

Completeness4/5

The core domain—authoritative structured data access—is extremely well covered: universal routing, grounded mode, deep research, entity profiles, comparisons, claim validation, resolution, discovery, and suggestions. Minor gaps exist (no explicit tool for fetching a pipeworx:// citation URI directly, no update operation for subscriptions), but agents can work around these via the router and existing subscription lifecycle tools.

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