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Public Suffix List

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 readOnly, openWorld, idempotent, and non-destructive hints. The description adds essential behavioral details that annotations do not cover: the verdict enumeration, the distinction between could_not_verify and unsupported, the verification_error structure, and the 'must not be shown as evidence' instruction. This is rich supplementary context.

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 longer than average but every sentence earns its place: example invocations, usage guidance, routing behavior, verdict meanings, and error semantics are all included without redundancy. It is well-structured, starting with examples and usage before diving into return details.

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 having no output schema, the description fully explains what the tool returns: verdict types, grounded/structured values with citations, reasoning, and error semantics. It also covers edge cases and caller responsibilities, making it complete for a tool of this complexity.

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?

The input schema describes both parameters (claim and tolerance_pct) with examples and defaults. The description adds meaningful extra guidance on tolerance_pct, explaining how to use it for hallucination detection and how it overrides implied wording, which goes beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as natural-language claim verification against authoritative sources, with example queries and a specific verb. However, it does not explicitly distinguish itself from sibling tools, so it falls short of a perfect 5.

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?

The description provides explicit usage guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the routing logic for company-financial claims vs. other factual claims, giving clear context for when to use 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

C2.9/5.0
Disambiguation2/5

Multiple tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all query data in similar ways. Prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) are numerous and confusingly similar. Agents will struggle to choose the right tool.

Naming Consistency2/5

Tool names follow no consistent pattern. Some use snake_case like ai_visibility_check, others are generic single words (parse, remember, forget). There is no uniform verb_noun structure, mixing descriptive names (entity_profile) with vague ones (list_version).

Tool Count3/5

35 tools is a large set for a server named 'Public Suffix List', but the actual domain (comprehensive data platform) may justify many tools. However, the count feels heavy for the apparent scope of the server, with many niche prediction market tools.

Completeness4/5

The tool surface covers a wide range: data query, comparison, subscription, memory management, and claim verification. However, there are gaps in data modification (no update/delete for records) and some prediction market features have no direct counterparts. Overall, the set is fairly complete for its data-fetching purpose.

Resources