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Glama

Openiban

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

A4.6/5.0
Behavior5/5

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

The description goes well beyond annotations by disclosing the exact verdict enum, the distinction between 'could_not_verify' (check did not happen, must not be evidence) and 'unsupported' (no source exists), the auto-routing behavior, and the use of verbatim evidence with pipeworx:// citations. This is significant operational context that annotations do not 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 long but well-structured: trigger phrases first, then scope/routing, then output/verdicts, then a highlighted caller warning. No sentence is filler; the length is justified by the tool's complexity. It earns a 4 rather than a 5 because it could be tightened slightly without losing meaning.

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?

For a tool with no output schema, the description covers the essential return elements (verdict, actual value, citation, reasoning, verification_error) and explains the critical failure semantics. It also addresses the routing to sources (SEC EDGAR + XBRL vs. grounded pipeline) and the unified nature of the tool. This is complete for an agent to invoke it correctly.

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% (both parameters documented in the schema), so the baseline is 3. The description adds value by explaining tolerance_pct semantics beyond the schema: it overrides claim-wording tolerance and recommends 1–2% for hallucination detection. This extra guidance justifies a 4.

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 uses a clear verb ('validate claim', 'fact check', 'verify the claim') and identifies the exact resource: natural-language claims verified against authoritative sources. It also distinguishes the tool's scope (company-financial claims vs. any other factual claim) and notably states it replaces 4–6 sequential calls, differentiating it from sibling research tools.

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?

Explicitly states when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also gives routing guidance for company-financial vs. other claims. However, it does not name sibling tools as alternatives or provide explicit 'when not to use' exclusions, so it falls just short of a 5.

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
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and the polymarket_* family (edges, arbitrage, edge_tracker, fill_risk) blurs together for an agent trying to pick one. entity_profile and recent_changes also both pull company data, and ai_visibility_check vs scan_competitor_ai_presence are single-vs-multi variants of the same probe.

Naming Consistency3/5

Naming is mostly snake_case and readable, with many verb_noun forms (validate_iban, generate_llms_txt, resolve_entity). However, there are bare verbs (remember, forget, recall, subscribe, unsubscribe), noun phrases (entity_profile, recent_changes, pipeworx_trending), and inconsistent prefixes (ask_ vs polymarket_ vs suggest_) that break a clear pattern.

Tool Count3/5

33 tools is borderline-heavy for a data-research API, but the bigger issue is that the server is named Openiban yet contains only two IBAN tools and 31 unrelated Pipeworx/data tools. The count feels bloated and misaligned with the server's apparent identity, though not extreme enough for a 1 or 2.

Completeness2/5

For the Pipeworx data-research domain the surface is quite rich (query, deep research, entity profiles, comparisons, subscriptions, memory). For the server's stated IBAN purpose, only validate and suggest_iban exist — no generation, parsing, batch checks, or bank detail coverage — so the tool set is severely incomplete relative to the server name.

Resources