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Glama

Abuseipdb

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?

Annotations already declare read-only, open-world, idempotent, and non-destructive hints, and the description adds critical non-obvious behaviors: the full verdict enum, the distinction between 'could_not_verify' and 'unsupported' (including how callers must present them), the citation format (pipeworx://), and the two execution paths (SEC EDGAR/XBRL vs. grounded pipeline). This goes far beyond the annotations and materially changes how an agent should interpret and handle results.

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 every sentence earns its place. It front-loads the purpose with user phrasings, then provides usage, routing, return value details, and important caller warnings. The structure is logical, though slightly dense; it could be tightened without losing content, but it's not excessively verbose for the complexity it covers.

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?

With no output schema, the description carries the full burden of explaining return semantics. It lists all verdicts, describes the actual-value + citation + reasoning output, and explicitly defines error states ('could_not_verify' vs 'unsupported') with guidance on how to present them. It also accounts for both structured and grounded pipelines, making the tool's behavior fully comprehensible to an agent without external documentation.

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% and both parameters have descriptive texts. The description adds extra semantic value by explaining that tolerance_pct overrides the wording-implied tolerance, gives a concrete use case ('set 1–2 for hallucination detection'), and clarifies the claim format with examples. This elevates it above the bare schema 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 concrete example phrasings ('Is it true that...', 'fact check'), then names the resource: 'natural-language claim verification against authoritative sources.' It clearly distinguishes itself from siblings by focusing on fact-checking and claim verification, and even notes it replaces 4–6 sequential calls, making its unique role explicit.

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?

The description gives a clear trigger: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains routing for company-financial claims versus other factual claims, which is an important when-to-use nuance. It doesn't explicitly name sibling tools to avoid or say 'don't use this for open-ended research,' but the guidance is strong and can be inferred.

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

A3.5/5.0
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical in current behavior, ai_visibility_check is subsumed by scan_competitor_ai_presence, and the six Polymarket-related tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) blur together. An agent would struggle to pick the right tool without reading the very long descriptions in full.

Naming Consistency2/5

Naming conventions are mixed: verb_noun (check_ip, get_blacklist, report_ip), bare nouns (entity_profile, deep_research, bet_research), prefix families (ask_pipeworx*, polymarket_*, pipeworx_*), and standalone verbs (remember, recall, forget, subscribe). The server itself is named Abuseipdb but the vast majority of tools are Pipeworx-related, making the naming feel disconnected from the server identity.

Tool Count2/5

At 34 tools, this exceeds the 25+ threshold for a heavy tool surface. The set is a grab bag of unrelated domains—AbuseIPDB, Pipeworx data access, Polymarket betting, memory management, AI visibility, dependency scanning, and llms.txt generation—rather than a cohesive single-purpose server. Many meta-tools (suggest_questions, discover_tools, pipeworx_trending, pipeworx_feedback) further inflate the count.

Completeness3/5

The de facto Pipeworx data-access domain is well covered with router, grounded, deep-research, entity, comparison, validation, resolution, change-feed, and search-within tools, plus memory and subscription lifecycle support. However, the nominal AbuseIPDB purpose has only three tools (check, blacklist, report) with no categories, bulk lookup, or detailed report features, and some subdomains lack updates (e.g., no subscription editing), creating notable gaps.

Resources