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Ship On Friday

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?

Annotations already declare safe read-only, idempotent behavior, but the description adds significant context: the meaning of 'could_not_verify' (check did not happen) vs 'unsupported' (no source), and warns callers not to treat could_not_verify as evidence. It also details the two routing paths and the structured vs grounded pipeline behavior, going well beyond annotations.

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 well-structured, front-loaded with trigger phrases and organized into purpose, behavior, and caller warnings. Every sentence contributes useful information, though it could be trimmed slightly without losing meaning. It earns a 4 due to the density of valuable content.

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?

Despite no output schema, the description thoroughly covers return values (verdicts), citation format, and error semantics. It addresses the tool's complexity with two operational modes, explains failure handling, and provides enough context for an agent to use it correctly. Complete for the tool's scope.

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% and both parameters have descriptions, but the description adds extra semantics: tolerance_pct overrides the implied tolerance, with an example of 'set 1–2 for hallucination detection,' and explains the default behavior (implied by wording, capped at 5). This adds value beyond the schema fields.

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 states the tool's purpose: 'natural-language claim verification against authoritative sources.' It provides trigger phrases and explicitly distinguishes between financial claims (via SEC EDGAR+XBRL) and other claims (grounded pipeline), making its scope unambiguous and differentiating it from siblings like ask_pipeworx_grounded.

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 says 'Use whenever the agent needs to check whether something a user said is factually correct,' providing clear when-to-use. It also notes it 'Replaces 4–6 sequential calls,' implying an efficiency advantage. However, it does not explicitly name alternative tools or give when-not-to-use conditions, 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

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is some overlap among data query tools like ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, compare_entities, and recent_changes. However, detailed descriptions and different use cases help an agent distinguish them, so it is mostly clear.

Naming Consistency5/5

All tool names follow a consistent lower_snake_case pattern with a verb_noun style (e.g., ask_pipeworx, list_subscriptions, validate_claim). There are no mixed conventions, making it predictable and easy to understand.

Tool Count4/5

With 31 tools, the server covers a broad domain of data queries, prediction markets, memory, and subscriptions. While slightly more than typical, each tool earns its place and the count is reasonable for the comprehensive platform scope.

Completeness5/5

The tool surface is extensive, covering data querying, analysis, entity resolution, comparison, change tracking, memory, subscriptions, and more. There are no obvious gaps; it supports a wide range of user intents for the server's purpose.

Resources