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Glama

Activecampaign

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 readOnly=true, openWorld=true, idempotent=true, destructive=false. The description goes well beyond these by explaining the verdict set (confirmed, approximately_correct, refuted, inconclusive, unsupported, could_not_verify), detailing the critical distinction between could_not_verify (not evidence) and unsupported (no source found), and revealing the internal routing logic. No contradiction with 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 dense and long, but every section earns its place: trigger phrases, pipeline routing, verdict list, and important caller caveats. It is front-loaded with examples and clear statements. Slightly over-stuffed with enumerated verdicts and error semantics, but still structured and scannable for an AI agent.

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 and the absence of an output schema, the description fully covers behavioral expectations: what it validates, how it routes claims, the exact return elements (verdict, value, citation, reasoning), and the meaning of special statuses. It leaves no important gap for an agent to misuse the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with both `claim` and `tolerance_pct` adequately described in the schema. The description adds example claim formats and mentions tolerance override behavior, but these are illustrative rather than necessary semantics. Baseline 3 is appropriate because the schema carries the parameter documentation burden and the description doesn't add substantial extra meaning.

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 explicitly states the tool's function: natural-language claim verification against authoritative sources, with trigger phrases like 'fact check' and 'verify the claim that'. It clearly distinguishes this from sibling tools by describing a single-call replacement for a 4-6 step sequential pipeline, making its purpose unmistakable.

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?

The description gives explicit guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It further differentiates between company-financial claims (SEC EDGAR/XBRL path) and other factual claims (grounded pipeline), and mentions it replaces multiple sequential calls. This provides strong when-to-use and alternative context, though it doesn't explicitly list exclusions.

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

B3.3/5.0
Disambiguation2/5

Several tools occupy nearly the same role: ask_pipeworx and ask_pipeworx_beta are described as behaviorally identical, ask_pipeworx_grounded and deep_research both route questions across the same large catalog, and the Polymarket tools overlap heavily in intent. The ActiveCampaign list/get tools are distinct, but an agent facing 37 tools would frequently struggle to choose the right research or betting tool.

Naming Consistency2/5

Some clusters are internally consistent (list_*, ask_pipeworx_*, polymarket_*), but the server overall mixes bare verbs like remember and forget, noun phrases like entity_profile and deep_research, and brand-prefixed names like pipeworx_trending and polymarket_arbitrage. There is no unified naming convention across the tool set.

Tool Count2/5

37 tools is excessive for an ActiveCampaign integration, and only 6 of them actually relate to ActiveCampaign. The rest are a broad Pipeworx data, research, and prediction-market utility suite, so the count is not well-scoped to the server's stated purpose.

Completeness1/5

As an ActiveCampaign server, the surface is severely incomplete: it only provides read-only list/get operations and no create, update, delete, send, tag, or workflow-management tools. The unrelated Pipeworx tools may be feature-rich, but they do not fill the gaps in the named ActiveCampaign domain.

Resources