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Glama

Vat

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

A4.8/5.0
Behavior5/5

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

Annotations declare readOnly and idempotent, but the description adds significant behavioral detail: the dual-path routing (structured vs. grounded), the response structure (verdict, value, citation, reasoning), and the critical semantic distinction between could_not_verify (check did not happen) and unsupported (source not covered).

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 lengthy but information-dense, packing trigger phrases, routing logic, output details, and caller warnings into one paragraph. Every sentence adds value, though it could be slightly better structured with section breaks for easier scanning.

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?

Despite no output schema, the description fully covers return values (verdict types, actual value, citation, reasoning) and edge-case semantics (could_not_verify with verification_error). It also explains how the tool integrates into workflows (replacing 4-6 sequential calls), making it complete for a complex tool.

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%, so a baseline of 3 applies. The description adds meaningful context beyond the schema by explaining that tolerance_pct overrides claim-wording implied tolerance and recommending 1-2 for hallucination detection, enriching the parameter's semantics.

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 clearly states the tool's purpose: verifying natural-language claims against authoritative sources, with trigger phrase examples. It distinguishes itself from siblings by describing its specialized behavior (structured SEC path vs. grounded pipeline) and noting it replaces multiple 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?

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct', and differentiates between company-financial and other claims. It also clarifies the meaning of outcome verdicts (could_not_verify vs unsupported), providing clear guidance on interpretation.

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

A3.7/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying catalog, and ask_pipeworx_beta is currently identical to ask_pipeworx. ai_visibility_check and scan_competitor_ai_presence also overlap, and entity_profile vs recent_changes cover similar ground. The descriptions are detailed, but an agent would frequently struggle to pick the right tool.

Naming Consistency3/5

All names use snake_case, but the pattern is inconsistent: some are verb-first (validate_vat, list_vat_formats, resolve_entity), some are noun-first (entity_profile, polymarket_edges, recent_alerts), and some are product-branded (ask_pipeworx, pipeworx_feedback). It's readable but does not follow a single predictable verb_noun convention.

Tool Count2/5

33 tools is well beyond the 25+ threshold for a coherent set, especially for a server named 'Vat' where only two tools (list_vat_formats, validate_vat) relate to the apparent purpose. The bulk forms a broad data-research platform that would be more appropriately split into separate VAT and Pipeworx servers.

Completeness4/5

For the actual data-research domain, coverage is strong: discovery, single lookup, grounded answers, deep multi-source research, entity resolution, comparisons, claim verification, memory, subscriptions, and prediction-market tooling are all present. The only clear gap is that the VAT-specific surface is minimal (format validation only, no registration/VIES check), but the overall functional surface is otherwise quite complete.

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