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Glama

Woocommerce

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

A4.6/5.0
Behavior5/5

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

Annotations already provide safety hints (readOnly, idempotent), and the description adds critical behavioral nuance: it defines the meaning of 'could_not_verify' as a failed check (not evidence), distinguishes 'unsupported' from inconclusive, and explains the two data paths. This goes well beyond the structured annotations and prevents misinterpretation of 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 lengthy but each sentence earns its place—it covers usage, pathways, return values, and edge cases. It is front-loaded with user phrasing examples and a concise one-liner definition. Slightly verbose but justified by the tool's complexity and the absence of an output schema.

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 thoroughly covers return values (verdict enum), evidence with citations, reasoning, and error semantics. It also explains how the tool handles different claim types. An agent would understand exactly what to expect and how to interpret results, making it complete for this 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% for both parameters, but the description enriches the semantics: it gives concrete examples for 'claim' and explains how 'tolerance_pct' overrides implied wording with a recommended range for hallucination detection. This adds actionable meaning 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 clearly names the action ('natural-language claim verification') and the resource ('authoritative sources'), with explicit query patterns like 'Is it true that…' and 'fact check'. It distinguishes itself from sibling tools by focusing on verifying claims and mentioning 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 Guidelines4/5

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

The description gives explicit context: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also outlines the two internal pathways (SEC EDGAR for financial claims, grounded pipeline for others), but it does not name an alternative tool or explicitly state when not to use it, so it stops short of a 5.

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

The five WooCommerce tools are clearly distinct, but the remaining 31 Pipeworx tools contain several overlapping query/research entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim) that an agent would struggle to tell apart without deep inspection. The context of the server also misleads: an agent expecting WooCommerce tools must navigate a much larger unrelated research toolkit.

Naming Consistency2/5

The woo_* tools follow a clean verb_noun pattern, but the bulk of the set uses wildly mixed conventions: bare verbs (forget, recall, subscribe), noun phrases (entity_profile, recent_changes), generic names (ask_pipeworx, discover_tools), and vendor-prefixed variants (pipeworx_trending, polymarket_edges). There is no single naming system across the server.

Tool Count2/5

36 tools is far too many for a server whose apparent purpose is WooCommerce store access; only 5 tools actually relate to WooCommerce. The remaining 31 are an unrelated general-purpose data research suite, making the toolkit feel bloated and mis-scoped.

Completeness1/5

The WooCommerce surface is severely incomplete: it only supports read operations (get/list for products, orders, customers). There are no create, update, delete, refund, or other write operations, so an agent cannot actually manage a store. The unrelated Pipeworx tools do not fill these gaps.

Resources