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Glama

Rba

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?

Descriptions goes well beyond annotations by explaining the two routing paths, the meaning of each verdict, and the critical distinction that 'could_not_verify' means the check did not happen and must not be presented as evidence. This is essential behavioral context that annotations alone (readOnlyHint, openWorldHint, idempotentHint) cannot convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured: examples first, then purpose, then routing, then return values, then important caveats in a clear callout. Every sentence serves a purpose—no filler—and the IMPORTANT section is appropriately highlighted.

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 tool's complexity and the absence of an output schema, the description fully explains return verdicts, evidence citations, and reasoning. It covers the two processing paths, the meaning of each verdict, and the error condition. It also frames the tool as a replacement for multiple sequential calls, giving the agent a complete understanding of scope and behavior.

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% so parameters are already described. The description adds value by explaining how tolerance_pct overrides the wording-implied tolerance and suggesting 1–2 for hallucination detection, plus the default cap of 5. This extra guidance helps the agent set the parameter correctly without relying solely on the 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 opens with natural-language query examples then defines the action clearly: 'natural-language claim verification against authoritative sources.' It distinguishes this tool from siblings by focusing on fact-checking with a verdict output, and mentions the specific structured fast path for company-financial claims vs. the grounded pipeline for everything else.

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 explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct,' giving clear context. It also explains when the SEC EDGAR fast path applies versus falling through to the grounded pipeline, but it does not name alternative tools or state when not to use this tool, so it stops short of full when-not/alternatives guidance.

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

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as entry points for data questions, and ask_pipeworx_beta currently behaves identically to ask_pipeworx. Entity-focused tools (entity_profile, compare_entities, recent_changes, ai_visibility_check, scan_competitor_ai_presence) also blur together even with lengthy descriptions.

Naming Consistency4/5

All tools use lowercase snake_case, which is consistent and readable. There is some variation between verb-first names (discover_tools, resolve_entity) and noun-first names (entity_profile, polymarket_arbitrage), plus the ask_pipeworx variant family, but the pattern is predictable overall.

Tool Count2/5

35 tools is heavy, and the scope sprawls far beyond the 'Rba' server name: only 4 tools relate to the Reserve Bank of Australia, while the rest cover a universal data router, prediction markets, memory, subscriptions, AI visibility, npm scanning, and more. Several meta-tools (ask_pipeworx, deep_research, discover_tools, suggest_questions) duplicate the discovery/routing role, making the count feel inflated.

Completeness3/5

For the RBA-specific subdomain, coverage is solid: directory lookup, series fetching, cash rate, and exchange rates. For the broader data-research domain most tools imply, coverage is quite comprehensive (routing, grounded answers, entity resolution, fact-checking, monitoring, memory), but the server's stated identity is unclear, and the beta duplicate tool adds noise rather than filling a real gap.

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