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Glama

Banxico

by io.github.pipeworx-io
OverviewAdmin

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

A4.8/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses critical behavioral nuances: the meaning of 'could_not_verify' (check did not happen, not evidence), 'unsupported' (no source exists), and the routing logic between SEC EDGAR and the grounded pipeline. It also warns callers that 'could_not_verify' must not be shown as a verdict, adding valuable guardrails. No contradictions with annotations.

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 a single long paragraph, but it is densely packed with actionable information and logically sequenced: example queries, use case, routing, output description, and caveats. Every sentence earns its place, and the important error-handling note about 'could_not_verify' is front-loaded near the end but clearly flagged as IMPORTANT. Slightly verbose but not wasteful.

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 the return values (verdict types, actual value, citation, reasoning) and edge cases (could_not_verify, unsupported). It also covers the tool's scope (what claim types are handled) and the routing behavior, making it self-contained for a complex tool. This is a complete description.

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 schema already covers both parameters at 100% with descriptive text, but the description adds extra semantics: it explains that 'tolerance_pct' overrides the tolerance implied by claim wording and suggests setting 1–2 for hallucination detection. It also provides concrete examples of valid 'claim' inputs, enriching comprehension beyond the bare schema.

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-verification service with specific verbs like 'fact check' and 'verify the claim that', and it differentiates itself from siblings by detailing the structured SEC EDGAR fast path for financial claims versus the grounded pipeline for all others. It even enumerates the exact verdict types returned, leaving no ambiguity about what the tool does.

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 states explicitly when to use the tool ('Use whenever the agent needs to check whether something a user said is factually correct') and distinguishes between company-financial and other factual claims with separate routing. It further clarifies that 'Replaces 4–6 sequential calls', which helps an agent decide it's the right tool for a complex verification task. No alternative tools are named, but the conditional logic effectively excludes cases where simpler tools would suffice.

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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