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Data Va

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / properties / tolerance_pct
      Added value: +{
+  "description": "Max 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.",
+  "type": "number"
+}
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond annotations, the description discloses internal routing logic (financial vs. other claims), the return structure (verdict, value, citation, reasoning), and critical behavioral caveats (could_not_verify means check did not happen, unsupported means no source). This adds substantial context that annotations do not cover, and it does not contradict any annotation.

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 longer than typical but densely packed with valuable information. It front-loads trigger phrases and proceeds through routing, outputs, and caveats. Every sentence adds something, though some repetition of 'grounded' could be tightened. It earns a high score for effectiveness, but loses a point for slight verbosity.

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?

Given the absence of an output schema, the description fully explains return values and their meanings, including the nuanced distinction between 'could_not_verify' and 'unsupported'. It covers edge cases, error handling, and the tool's overall role, making it complete for an agent to invoke and interpret results correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema already documents both parameters (100% coverage), but the description adds meaningful semantics: tolerance_pct is explained as overriding the implied tolerance with practical guidance (set 1–2 for hallucination detection) and a default cap of 5. The claim parameter is illustrated with examples, enriching the schema description.

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 identifies the tool as a claim verifier with specific verb ('verify') and resource ('natural-language claim against authoritative sources'). It distinguishes itself by describing the two distinct pipelines (SEC EDGAR for financial claims, grounded pipeline for others) and states it replaces multiple sequential calls, setting it apart from 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?

Explicit guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It further clarifies when the structured path vs. grounded pipeline applies, and explains the semantic difference between 'could_not_verify' and 'unsupported', preventing misuse. The alternatives (sequential calls) are implied via 'Replaces 4–6 sequential calls', but usage context is strong.

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

A3.9/5.0
Disambiguation2/5

Several tool clusters are near-duplicates or easy to confuse: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical (beta currently matches stable exactly), while polymarket_edges, polymarket_arbitrage, and bet_research all scan prediction-market opportunities with overlapping outputs. entity_profile/compare_entities/recent_changes and ai_visibility_check/scan_competitor_ai_presence add further redundancy. Despite detailed descriptions, the boundaries require careful reading, so an agent is likely to misselect.

Naming Consistency3/5

All names are snake_case and readable, with useful prefixes like ask_, polymarket_, pipeworx_, and scan_. However, conventions are mixed: verb_noun (ask_pipeworx, list_subscriptions, resolve_entity) coexists with bare nouns (datasets, metadata, query) and noun-first compounds (entity_profile, bet_research, deep_research). There is no single predictable pattern.

Tool Count2/5

34 tools is above the 25-tool threshold, and the set spans multiple unrelated domains such as data routing, prediction markets, memory, subscriptions, Virginia Open Data, AI visibility, and npm dependency checks. Several tools are effectively wrappers or near-overlaps that could be consolidated, e.g., ai_visibility_check vs scan_competitor_ai_presence and polymarket_edges vs polymarket_arbitrage. The surface feels bloated for a single server.

Completeness4/5

Within its main sub-domains the set is solid: memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, research has resolve_entity/compare_entities/entity_profile/recent_changes/validate_claim, and Polymarket has detection/arbitrage/fill-risk/edge-tracking. Minor gaps exist — no tool to fetch a raw pipeworx:// record, no write/update for Virginia Open Data, and no trade execution for prediction markets — but these do not create dead ends for a research-focused agent.

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