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Glama

Stripe_connect

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

A4.4/5.0
Behavior5/5

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

Beyond the read-only annotations, the description discloses the full verdict taxonomy, the crucial distinction between could_not_verify (failure, not evidence) and unsupported (no source coverage), and the internal EDGAR vs. grounded pipeline routing. It also warns callers about misusing could_not_verify, which is valuable 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 dense but well-structured: trigger phrases, use case, routing, return values, warnings. Every sentence provides necessary context, though it is longer than the ideal two-sentence style. The front-loading of trigger phrases helps quick 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?

With no output schema, the description fully compensates by listing all six verdict values, the return of actual value with citation and reasoning, and the verification_error structure. It also covers the two-path routing and the performance benefit, leaving no major gaps for a caller.

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?

The input schema provides 100% coverage with detailed descriptions for both parameters, so the tool description doesn't need to add parameter-level detail. The baseline of 3 is appropriate; the description itself adds no param semantics beyond what the schema already states.

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 exact natural-language trigger phrases ('fact check', 'verify the claim') and defines the resource as authoritative sources, making the tool's purpose unmistakable. It distinguishes itself from sibling research tools by focusing on verdict-based claim verification (confirmed/refuted/etc.) and explicitly replaces a 4–6 call pipeline.

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?

It explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and provides routing rules for financial vs. other claims. However, it does not name specific sibling tools or state when not to use it, so it stops just short of a perfect score.

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

The set has several near-duplicate entry points: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, while ask_pipeworx_grounded/deep_research and bet_research/polymarket_edges overlap heavily. Verbose descriptions help in isolation, but an agent must choose between many similar-looking research and prediction-market tools before it can act.

Naming Consistency2/5

Naming mixes bare verbs (remember, forget, subscribe), prefixed families (pipeworx_*, polymarket_*, stripe_*), and descriptive noun-style names (entity_profile, validate_claim) with no single verb_noun pattern. Each cluster is internally consistent, but the overall set is unpredictable.

Tool Count2/5

37 tools is excessive for a server named Stripe_connect, especially since only 6 tools are actually Stripe-related and the rest are a sprawling Pipeworx data-research and prediction-market stack. The count would be heavy even for the broad research domain, and it is a serious scope mismatch for the stated name.

Completeness1/5

For the Stripe domain implied by the server name, the surface is severely incomplete: it is read-only (get/list) with no way to create customers, take payments, issue refunds, update invoices, or manage subscription lifecycles. The non-Stripe research tools are broad, but that does not fill the payment workflow gap.

Resources