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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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context beyond the annotations: it explains the two processing paths (structured vs. grounded), the meaning of each verdict, and the critical distinction that 'could_not_verify' indicates a failure and carries verification_error — it is not evidence for/against the claim. This helps the agent understand the tool's failure modes and open-world behavior.

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 information-dense. The trigger phrases, routing logic, verdict list, error semantics, and efficiency note each serve a purpose. The structure is logical: purpose → routing → output → caller warnings. It could be slightly trimmed (e.g., 'grounded pipeline' is mentioned twice), but the length is justified by the tool's complexity.

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 carries the full burden of explaining return values—and it does this thoroughly. It names the full set of verdicts, describes the structured/grounded actual value with citation, and explains the verification_error structure. It also covers edge cases like 'unsupported', which maps to open-world behavior, and warns against misusing 'could_not_verify'. This is exceptionally complete for a complex tool.

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 description coverage is 100%, so the parameters are already well-documented. The description itself does not add new parameter semantics beyond what the schema provides—it mentions tolerance only implicitly via the structured math. The schema's descriptions for 'claim' and 'tolerance_pct' are already detailed with examples and defaults, so a baseline of 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 a clear, specific purpose: 'natural-language claim verification against authoritative sources.' It provides numerous trigger phrases ('fact check', 'verify the claim that…') that instantly clarify when to use this tool. It also distinguishes itself from siblings by explicitly stating it replaces a 4–6 step sequential pipeline, which is unique among the sibling tools.

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?

It explicitly states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It gives detailed routing guidance for company-financial claims (SEC EDGAR + XBRL fast path) versus all other claims (grounded pipeline). It also warns against misinterpretation of 'could_not_verify' and explains the difference between 'could_not_verify' and 'unsupported', which is crucial for correct usage.

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

A3.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route queries to the same data sources with only subtle differences. Similarly, bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_edge_tracker all analyze prediction markets, making it hard for an agent to pick the right one without deep reading of descriptions.

Naming Consistency3/5

Tool names follow a mix of patterns: some are verb_noun (generate_llms_txt, list_subscriptions), some noun_verb (ai_visibility_check, bet_research), and some are just nouns (datasets, metadata). The ask_pipeworx family has consistent prefixes but suffixes vary. Overall readable but inconsistent.

Tool Count2/5

34 tools is excessive for a coherent server. The server tries to be a Swiss Army knife covering data lookup, prediction markets, Delaware open data, memory, subscriptions, and misc tools like generate_llms_txt and scan_dependency. Many tools feel tacked on, and the count makes it unwieldy for an agent to navigate.

Completeness3/5

The server covers multiple domains thoroughly (data query via Pipeworx variants, prediction markets with arbitrage and edges, Delaware open data, memory, subscriptions). However, there are gaps: no tool for managing custom pipelines or for updating data. For the broad scope, it is decent but not fully comprehensive.

Resources