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holidays

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. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds critical behavioral nuance beyond annotations: could_not_verify is a non-result containing verification_error and definitively not evidence for or against the claim; unsupported means no source coverage. It also discloses the two distinct execution paths (structured financial vs. grounded general), which is value well beyond what annotations provide.

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 lengthy but each sentence serves a purpose: trigger phrases, when-to-use, path selection, verdict semantics, and the efficiency benefit. The first sentence front-loads the core purpose. It is somewhat run-on and could be tightened, but there is no redundant filler.

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 enumerates all six possible verdict labels, explains the return structure (actual value + pipeworx:// citation + reasoning), and covers edge cases with clear guidance. It also describes both operation modes (structured vs. grounded) and the tolerance behavior. This gives an agent sufficient information to invoke the tool and interpret its results correctly in varied scenarios.

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% for both parameters, so the baseline is 3. The description adds meaningful context beyond the schema: tolerance_pct overrides claim-wording tolerance and is capped at 5% by default, and it references 'exact percent-delta math' for financial claims, explaining how tolerance is applied. The claim parameter is also exemplified with realistic phrasing, enhancing usability without repeating schema text.

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 concrete trigger phrases and clearly states the tool performs 'natural-language claim verification against authoritative sources.' It specifies a verb (verify/validate) and a resource (factual claims), and distinguishes itself from sibling research tools by focusing on fact-checking with a verdict-based output. It also notes it replaces 4–6 sequential calls, reinforcing its unique role.

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?

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct.' It provides a branching rule: company-financial claims use the SEC EDGAR + XBRL fast path, while all other factual claims fall through to a grounded pipeline. It also conveys when not to use it implicitly by positioning it as a consolidated replacement for multi-step lookup flows.

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
Disambiguation1/5

Multiple tools serve near-identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical today, ask_pipeworx_grounded and deep_research overlap heavily, and bet_research/polymarket_edges/polymarket_arbitrage all scan prediction-market opportunities. An agent reading these names and descriptions cannot reliably pick one tool without reading very long descriptions.

Naming Consistency2/5

The names are all lower_snake_case, but the naming style is not consistent across the server: verb_noun (get_holidays, validate_claim), bare noun phrases (next_holidays, entity_profile, bet_research), is/are predicates (is_today_holiday), and separate pipeworx/polymarket prefixed groups. There are recognizable subgroups, but no coherent naming convention unifies the full tool surface.

Tool Count2/5

34 tools is over the healthy range and the majority have nothing to do with holidays; they belong to a broader Pipeworx data, prediction-market, and subscription platform. The holiday-specific surface is only three tools buried inside a much larger, unrelated toolkit, making the server feel overloaded and mislabelled.

Completeness4/5

For the actual holiday domain the core read workflows are covered: all public holidays by country/year, today's holiday status, and upcoming holidays. A small gap is the lack of a supported-country/region listing or date-range filtering, but these are easily worked around because get_holidays returns the full year set.

Resources