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Okx

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

A4.8/5.0
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. The description adds crucial behavioral disclosures beyond annotations: the precise meaning of 'could_not_verify' (check did not happen, must not be interpreted as evidence, includes verification_error payload) and 'unsupported' (no source found). It also explains return structure with verdict set, citation, and reasoning. This is significant value-added context.

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?

Description is long but every sentence adds essential information. It front-loads the purpose, then flows through routing, return values, and critical caller-facing warnings ('IMPORTANT for callers'). The use of paragraph breaks and structured enumeration helps readability. No filler or repeated annotation content.

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?

Even without an output schema, the description fully covers what the agent needs to interpret results: complete verdict enum, distinction between 'could_not_verify' and 'unsupported', citation format, and the dual-pipeline behavior. It also addresses edge cases and explicitly states what not to do with a verdict type. For a complex tool with only 2 parameters, this is highly self-sufficient.

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?

Schema description coverage is 100%, so baseline is 3. The description enriches both parameters substantially: for 'claim' it provides concrete examples; for 'tolerance_pct' it explains how it overrides implied tolerance, gives a default behavior ('capped at 5'), and recommends 1–2 for hallucination detection. This goes beyond the schema's minimal descriptions and aids correct invocation.

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?

Description clearly states the tool's core function ('natural-language claim verification against authoritative sources') with strong verb-resource pairing. It provides concrete query examples and distinguishes itself from generic search by explaining the structured SEC/XBRL fast path for company financials versus the grounded pipeline for all other claims. This sets it apart from sibling tools like ask_pipeworx or deep_research.

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 clarifies that it replaces 4–6 sequential calls, implying efficiency advantage over alternatives, but does not explicitly name sibling tools or provide 'when-not-to-use' guidance. The dual-path routing gives context on internal decision logic.

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

B3.2/5.0
Disambiguation2/5

The tool set mixes OKX exchange tools with a large set of Pipeworx data query tools and prediction market tools. Many tools overlap in purpose, e.g., ask_pipeworx, deep_research, and ask_pipeworx_grounded all answer questions but with different modes. OKX tools like ticker and tickers are clear but the overall set is confusing.

Naming Consistency2/5

Naming is inconsistent: OKX tools use single nouns or underscores (ticker, order_book), Pipeworx tools use verb phrases (ask_pipeworx, validate_claim), and prediction market tools use prefixed names (polymarket_arbitrage, bet_research). No consistent pattern.

Tool Count2/5

43 tools is excessive for a coherent server. The scope is unclear—combining exchange, data lookup, and prediction market tools into one server results in a cluttered surface.

Completeness2/5

The server tries to cover too many domains. OKX coverage is decent, but the inclusion of many unrelated tools (e.g., generate_llms_txt, scan_dependency) makes the set feel incomplete for any single purpose. Gaps exist in each sub-domain due to the broad scope.

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