Skip to main content
Glama

uiule

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. Added

TDQS

A4.4/5.0
Behavior5/5

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

The annotations already declare readOnly, openWorld, idempotent, and non-destructive, and the description adds crucial behavioral nuance: the distinction between 'could_not_verify' (check did not happen, not evidence) and 'unsupported' (no source), plus the existence of verification_error{stage,detail}. It also discloses the verbatim evidence and citation behavior, which goes well beyond annotation coverage.

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 average but every sentence contributes value: examples, routing rules, verdict list, and error semantics. It is front-loaded with the tool's purpose and usage intent. The length is justified by the tool's complexity and the absence of an output schema, though it could be tightened slightly.

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 thoroughly explains return values (verdict enum, actual value with citation, reasoning) and edge cases ('could_not_verify' vs 'unsupported'). It also describes the internal routing logic, which helps an agent set expectations. This is a complete description for a claim-verification tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides complete parameter descriptions for both 'claim' and 'tolerance_pct', including examples and the tolerance override semantics. The tool description itself does not add new parameter-level meaning; it only references 'exact percent-delta math' in passing. Since schema coverage is 100%, a baseline score of 3 is appropriate.

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 patterns ('Is it true that…', 'fact check') and directly states the tool's job: natural-language claim verification against authoritative sources. It clearly distinguishes its consolidated scope by noting it replaces 4–6 sequential calls, which separates it from siblings like resolve_entity or ask_pipeworx_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 explicitly instructs 'Use whenever the agent needs to check whether something a user said is factually correct.' It also distinguishes between company-financial claims (SEC EDGAR fast path) and any other factual claim (grounded pipeline), which is a practical routing guideline. It doesn't explicitly name sibling alternatives to avoid, but the 'Replaces 4–6 sequential calls' line implies when to prefer this tool.

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

All uiule 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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but ask_pipeworx and ask_pipeworx_grounded are very similar and could cause confusion. The multiple Polymarket tools are differentiated by their specific functions.

Naming Consistency3/5

Tool names are a mix of verb_noun (e.g., ask_pipeworx, get_verse) and noun_verb (e.g., polymarket_arbitrage, ai_visibility_check). While all use snake_case, the pattern is inconsistent.

Tool Count4/5

With 33 tools, the server is comprehensive but slightly large. Each tool seems justified, covering multiple domains like company data, prediction markets, Bible, and memory.

Completeness5/5

The tool surface is highly complete for a universal data server, including financials, drugs, patents, news, real estate, and more. Meta-tools like discover_tools and suggest_questions further enhance usability.

Resources