Skip to main content
Glama

Google_analytics

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?

The description goes well beyond annotations by explaining the fast path for SEC EDGAR + XBRL, the fallback grounded pipeline with verbatim evidence, and the crucial semantic distinction between 'could_not_verify' (system failure, NOT evidence) and 'unsupported' (no source found). It also discloses that it replaces 4–6 sequential calls, adding significant behavioral context.

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 fairly long but every sentence earns its place: trigger phrases, routing logic, return value enumeration, and error semantics. It is front-loaded with usage triggers and uses a structured flow. Slight redundancy in listing multiple verdicts could be trimmed, but overall density is high.

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 the tool's complexity (two pipelines, six verdicts, error payloads) and the absence of an output schema, the description is remarkably complete. It explains what is returned (verdict, actual value with citation, reasoning), defines the two special verdicts ('could_not_verify' and 'unsupported'), and describes the automated fallback behavior. No necessary context is missing.

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, giving a baseline of 3. The description adds value by explaining how tolerance_pct overrides the implied tolerance, recommending 1–2 for hallucination detection, and clarifying the default cap at 5. Claim examples also illustrate expected input format.

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 trigger phrases ('fact check', 'verify the claim that…') and states the tool's function: 'natural-language claim verification against authoritative sources.' It clearly distinguishes the tool from sibling question-answering tools by focusing specifically on verdicts for factual claims and describing the two verification pipelines.

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 guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains how to handle company-financial claims vs. other claims via routing, but it does not name alternative tools or state when NOT to use this tool, so it falls 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.

All Google_analytics 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

Several tools overlap heavily: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ask_pipeworx_grounded and deep_research both route queries to the same 5,529 tools, and ai_visibility_check vs scan_competitor_ai_presence are near-duplicates. Only the small ga_* subset is clearly distinct.

Naming Consistency2/5

Conventions are mixed: GA tools use a ga_ prefix, but the majority use arbitrary names like ask_pipeworx, deep_research, remember, scan_dependency, and polymarket_arbitrage. No consistent verb_noun pattern across the set.

Tool Count2/5

35 tools is heavy, and the vast majority (31) are unrelated Pipeworx utilities rather than Google Analytics functionality. Only 4 tools actually serve the stated GA purpose, making the count inappropriate for the server name.

Completeness2/5

For Google Analytics, the surface is minimal: list properties, metadata, realtime, and run report—no property management, user management, or data mutation. As a Pipeworx toolkit it's broad but lacks full lifecycle coverage for any single domain.

Resources