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Dockerhub

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?

Annotations declare read-only, idempotent, open-world, non-destructive; description adds critical behavior: the meaning of could_not_verify vs unsupported, warns that could_not_verify is not evidence and must not be shown as one, and mentions the exact percent-delta math. This goes beyond annotations and prevents misuse.

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 long but every sentence contributes: trigger phrases, usage rule, routing logic, return value, two important caveats, and efficiency claim. It's front-loaded with the purpose and has no 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?

The tool is complex (two paths, multiple verdicts, error states), and the description covers all essential behaviors: when to use, what it returns, the distinction between unsupported and could_not_verify, citation format. No output schema so this is necessary and sufficient.

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 has 100% coverage and already describes claim and tolerance_pct with examples and ranges. The description doesn't add new parameter semantics; it only references the fast path's math. Baseline 3 is appropriate when schema does the heavy lifting.

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 a specific verb+resource: natural-language claim verification against authoritative sources. It provides trigger phrases ('Is it true that…', 'fact check', 'verify the claim that…') and distinguishes from sibling research tools by focusing on returning a verdict (confirmed/refuted/etc.).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the two routing paths (SEC EDGAR for financial claims, grounded pipeline for others) and notes it replaces multiple sequential calls, giving clear context for when to choose this tool.

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

A3.6/5.0
Disambiguation2/5

Many tools overlap in purpose, e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, and validate_claim all perform data lookups with subtle differences. Polymarket tools also have overlapping scopes. The large number of tools with similar functions creates confusion.

Naming Consistency3/5

Names are inconsistent: some use verb_noun (get_image, list_subscriptions), others are descriptive phrases (ai_visibility_check, bet_research), and some are single words (forget, recall). No clear pattern.

Tool Count2/5

33 tools is high, and many are unrelated to Dockerhub. The server name suggests a focused Docker toolset, but the bulk of tools are for Pipeworx/Polymarket/data lookups, making the count excessive for the advertised domain.

Completeness2/5

As a Dockerhub server, it lacks basic Docker operations like push, delete, or manage repositories. As a general data toolset, it covers many domains but still misses some core operations (e.g., no tool for searching inside images).

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