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Glama

Rdap

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

A4.7/5.0
Behavior5/5

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

Annotations only declare readOnly/openWorld/idempotent and non-destructive, but the description goes much further: it explains the verdict set, distinguishes could_not_verify (check failed, not evidence) from unsupported (no source), and mentions verification_error{stage,detail}. This is critical behavioral context beyond 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 front-loaded with natural-language triggers and is well-organized, but it is fairly long. Every sentence provides useful information, so it earns its place, though a slightly tighter version could be imagined.

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?

There is no output schema, so the description carries full responsibility for explaining return values. It covers the two execution paths, the verdict list, the citation format, the error semantics, and the benefit of replacing multiple sequential calls. This is complete for a tool of this complexity.

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?

The input schema already covers both parameters, but the description adds real value by explaining tolerance_pct's override behavior, the default cap, and a concrete use case (1–2 for hallucination detection). This goes well beyond the schema descriptions.

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 uses specific verbs like 'fact check' and 'verify the claim that...' and clearly defines the tool as natural-language claim verification against authoritative sources. It distinguishes itself from sibling research tools by focusing on claim validation and even describes the two routing paths for company-financial vs. other claims.

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 explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and explains when the SEC EDGAR fast path vs. the grounded pipeline applies. However, it does not name alternative tools or explicitly state when not to use it, so it falls short of full guidance.

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

B3.1/5.0
Disambiguation2/5

The RDAP tools (domain, ip, asn, nameserver, entity) are distinct, but the larger Pipeworx collection creates significant overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates, and the polymarket_* family (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread) has heavily overlapping purposes. ai_visibility_check and scan_competitor_ai_presence also overlap.

Naming Consistency3/5

Most tools use snake_case with a descriptive verb_noun pattern (e.g., ask_pipeworx, discover_tools, recent_changes), but the set mixes short single nouns (domain, ip, asn, entity) with compound names, and there are oddities like generate_llms_txt and ask_pipeworx_grounded that deviate from a clear uniform convention.

Tool Count2/5

At 36 tools, the server is far heavier than expected for an RDAP service. The RDAP core only needs ~5 tools; the rest are an unrelated Pipeworx mega-suite (data queries, prediction markets, memory, subscriptions, feedback) that makes the server feel like a dumping ground rather than a focused toolset.

Completeness4/5

For the RDAP portion, coverage is complete: domain, IP, ASN, nameserver, and entity records are all present. The broader Pipeworx features also cover their own workflows (query, research, comparison, memory CRUD, subscription lifecycle), but the server's stated RDAP identity is muddied by these extras, and some integration points (e.g., no direct update for RDAP data, which is read-only anyway) are inherent limitations.

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