Skip to main content
Glama

iplookup

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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds substantial behavioral context: exact verdict vocabulary, evidence with pipeworx:// citations, structured verification_error for could_not_verify, and the explicit warning that could_not_verify is neutral evidence. No contradiction with annotations.

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 dense but logically organized: trigger phrases, usage directive, routing rules, return contract, and caller caveat. Every sentence adds value, though length is above average; the structure (with the IMPORTANT note) aids readability.

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 the return contract (verdict types, actual value, citation, reasoning), error semantics (verification_error), and both execution paths. It gives the agent enough context to invoke correctly and interpret results 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 description coverage is 100%, and the description supplements it with practical meaning: tolerance_pct default is implied by claim wording and capped at 5, with guidance to set 1–2 for hallucination detection. The claim parameter is illustrated with examples that clarify expected input.

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 explicit trigger phrases ('Is it true that…', 'fact check', 'verify the claim that…') and states the core function: natural-language claim verification against authoritative sources. It clearly distinguishes itself from sibling tools by describing a specialized two-path routing (SEC EDGAR for company financials, grounded pipeline for anything else) and by noting it replaces 4–6 sequential calls.

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

Usage Guidelines5/5

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

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It provides concrete routing rules (financial vs non-financial) and clarifies the meaning of special verdict states (could_not_verify vs unsupported), helping the agent decide when to call this tool and how to interpret results.

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

All iplookup 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

A3.7/5.0
Disambiguation2/5

Several tool families have genuine boundary ambiguity: ask_pipeworx and ask_pipeworx_beta are currently identical in behavior, and the five Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) all live in the 'find/pursue an edge' space, requiring an agent to parse very long descriptions to choose correctly. The IP, memory, and subscription tools are clearly distinct, but the overlap-prone families cause real misselection risk.

Naming Consistency3/5

All names are snake_case and each family is internally consistent (polymarket_*, ask_pipeworx_*, subscribe/unsubscribe, remember/recall/forget). However, conventions mix across the set: verb_noun (geolocate_ip, resolve_entity, validate_claim) coexists with noun-first names (entity_profile, pipeworx_feedback, recent_alerts) and bare verbs (remember, forget), so there is no predictable global pattern.

Tool Count2/5

At 33 tools the set exceeds the heavy threshold, and the server name 'iplookup' covers only 2 of them; the remaining 31 form a sprawling data-research, prediction-market, memory, and subscription platform with tangential utilities like generate_llms_txt and scan_dependency. The broad scope means few tools are individually useless, but the server reads as a kitchen sink rather than a focused toolkit.

Completeness3/5

As an IP-lookup service the surface is thin: only geolocation and ISP data, with no WHOIS, reverse DNS, proxy/VPN detection, or reputation records. As a data-research platform the surface is strong (universal query routing, grounded verification, entity profiles, comparisons, subscription lifecycle, memory). This lopsidedness makes the actual domain ambiguous and leaves the namesake use case under-covered.

Resources