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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

A4.5/5.0
Behavior5/5

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

Annotations already convey read-only, open-world, idempotent, and non-destructive traits. The description adds substantial behavioral context: the two-path routing, the exact meaning of could_not_verify (crucial caller warning not to treat it as evidence), the meaning of unsupported, and the inclusion of verification_error. This goes well beyond annotations and is essential for correct interpretation.

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?

Although the description is long, every sentence earns its place: example queries aid recognition, the purpose is stated clearly, the two paths are explained, the return values and critical verdict semantics are disclosed, and the efficiency benefit is noted. It is front-loaded and highly structured, making it easy for an agent to parse.

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 carries full responsibility for explaining return values. It lists all six verdicts, details the meaning of the two ambiguous ones, explains verification_error, and describes both processing paths. For a complex verification tool, this is exceptionally complete.

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 full descriptions for both parameters (claim and tolerance_pct) with examples and rules, so schema coverage is 100%. The description adds only a minor phrase 'exact percent-delta math' which doesn't materially enhance parameter understanding beyond the schema. 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?

The description opens with concrete example queries and then states a specific purpose: natural-language claim verification against authoritative sources. It clearly distinguishes the tool's scope (verifying factual claims) from general tools, and even describes the two internal pipelines (SEC EDGAR fast path vs. grounded pipeline), making the purpose unmistakable.

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 gives an explicit when: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains that all factual claims are covered, effectively ruling out the need for alternatives. However, it does not explicitly name when-not to use it or mention specific sibling tools, so it doesn't fully meet the 'alternatives' criterion for 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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is some overlap: ask_pipeworx and ask_pipeworx_grounded are very similar, and the multiple Polymarket tools could be confused. The memory tools (remember, recall, forget) are distinct.

Naming Consistency3/5

Tool names consistently use snake_case, but the pattern is not strictly verb_noun. Some names are descriptive phrases (e.g., scan_competitor_ai_presence), while others are straightforward (e.g., keyword_overview). Overall readable but not highly consistent.

Tool Count3/5

32 tools is on the high side for a single server, but the scope is broad (SEO, finance, FDA, betting, memory). The tool count feels slightly excessive, yet each tool appears justified by its specific use case.

Completeness4/5

The tool set covers a wide range of business research needs: SEO, SEC filings, FDA data, betting analytics, and memory. Minor gaps exist (e.g., no direct social media or HR data), but the coverage is impressive for a general-purpose data server.

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