Skip to main content
Glama

Ipqualityscore

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.

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses critical behavioral nuances: could_not_verify means the check did not happen and must not be treated as evidence, while unsupported means no source was found. It also explains the dual-path routing and the inclusion of pipeworx:// citations, providing substantial transparency beyond what annotations convey.

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 each sentence serves a purpose: trigger phrases, usage guidance, routing logic, verdict list, and important error-handling caveats. It is front-loaded with the core purpose and progressively adds detail, making it dense but efficient. Minor redundancy could be trimmed, but overall it earns its length.

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 there is no output schema, the description compensates fully by enumerating all possible verdicts, the composition of the return (actual value + citation + reasoning), and the distinct meanings of could_not_verify and unsupported. It also addresses error handling and the tool's advantage over sequential calls, making it complete for an agent to invoke 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?

The schema already fully describes both claim and tolerance_pct with examples. The description adds extra semantic value by explaining that tolerance_pct overrides the tolerance implied by the wording, suggesting 1–2% for hallucination detection, and clarifying the default behavior (implied by wording, capped at 5). This enriches the schema's baseline.

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 trigger examples and states the core function: 'natural-language claim verification against authoritative sources.' It clearly distinguishes this from siblings like ask_pipeworx by focusing on fact-checking and verdict generation, not general Q&A or research.

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?

Explicit guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates between company-financial claims (SEC EDGAR fast path) and all other claims (grounded pipeline), and even notes it replaces 4–6 sequential calls, giving clear context for when this tool is the right choice.

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

All Ipqualityscore 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

A3.5/5.0
Disambiguation2/5

Multiple natural-language query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, discover_tools, suggest_questions) have heavily overlapping purposes, and the descriptions rely on subtle caveats to differentiate them. Similarly, entity_profile vs compare_entities vs recent_changes and ai_visibility_check vs scan_competitor_ai_presence blur boundaries. Only the four check_* tools (email/ip/phone/url) are cleanly distinct.

Naming Consistency2/5

There are some consistent prefixes (check_*, polymarket_*, ask_pipeworx_*, pipeworx_*) but the overall set mixes verb_noun, noun_verb, and standalone adjectival names (deep_research, entity_profile, bet_research, validate_claim, recent_changes). The pattern is readable within families but chaotic across the whole surface, with no unified convention.

Tool Count2/5

35 tools is excessive for a server branded 'Ipqualityscore', especially since only 4 tools actually serve that fraud-checking domain. The rest sprawls into general data research, prediction-market analysis, memory management, subscriptions, and npm dependency scanning — a far larger scope than the name implies. This is a scattershot collection rather than a coherent offering.

Completeness2/5

The IPQS core domain is thin (only email, IP, phone, URL checks) and missing common fraud-screening operations like transaction scoring or domain reputation. Conversely, the Pipeworx side is over-complete with redundant query paths, while unrelated subsystems (memory, subscriptions, feedback) create dead ends that don't serve the server's apparent purpose. The lack of a clear domain makes genuine completeness impossible to assess or claim.

Resources