Skip to main content
Glama

Rhea

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, but the description adds critical behavioral nuance: the automatic routing between structured and grounded pipelines, the detailed verdict list, and the crucial distinction between 'could_not_verify' (a failed check, not evidence) and 'unsupported' (no source exists). This significantly exceeds what annotations convey.

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 well-structured: trigger phrases, core usage, routing explanation, return values, important caller warnings, and a performance note. It's front-loaded with the purpose statement and examples, and every sentence adds value, though slight trimming could improve scannability.

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 no output schema, the description thoroughly covers the return verdicts, the citation mechanism, error semantics, and the two distinct verification paths. It even explains edge cases like 'could_not_verify' and 'unsupported' in enough detail for an agent to handle results correctly. This is complete for the tool's complexity.

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?

Though schema coverage is 100%, the description adds meaningful context beyond the schema: tolerance_pct's interaction with claim wording, the override semantics, and concrete guidance (set 1-2 for hallucination detection). It also explains the default cap at 5%, which the schema alone doesn't imply.

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 explicitly states the tool's verb+resource: 'natural-language claim verification against authoritative sources.' It clearly distinguishes from sibling tools by focusing on fact-checking claims, and mentions the two distinct verification paths (structured SEC EDGAR vs grounded).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives an explicit when-to-use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also separates company-financial claims from other claims, but does not name specific alternatives or provide when-not-to-use guidance. The 'Replaces 4–6 sequential calls' hints at efficiency but not exclusion.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Rhea toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with only minor overlap between similar variants (e.g., ask_pipeworx variants) and related prediction market tools (bet_research vs polymarket_edges). Overall, an agent can reliably select the right tool.

Naming Consistency3/5

Tool names mix verb_noun (e.g., resolve_entity), noun_noun (e.g., entity_profile), and adjective_noun (e.g., recent_changes) patterns, plus a few single verbs. While readable, the lack of a uniform convention adds ambiguity.

Tool Count3/5

33 tools is large but justified by the broad domain coverage. The count is borderline high but still manageable with meta-tools (ask_pipeworx, deep_research) that reduce the effective surface.

Completeness4/5

The tool set covers a wide range of data sources and tasks, with meta-tools providing deep coverage. Minor gaps exist (e.g., no dedicated Kalshi-only tools, limited attention to non-Polymarket prediction markets), but the overall surface is thorough.

Resources