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Glama

Chargebee

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

A4.7/5.0
Behavior5/5

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

Annotations already cover safety (readOnly, idempotent, etc.), and the description adds critical behavioral context: verdict semantics, especially the crucial distinction between could_not_verify (check did not happen) and unsupported (no source found), plus the verification_error structure. This goes well beyond what annotations alone provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Although long, the description is dense and well-structured. It opens with trigger phrases, then explains routing, output, and critical caller warnings. Every sentence contributes essential information, and the 'IMPORTANT for callers' callout earns its place.

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, multiple verdicts, error semantics) and absence of an output schema, the description compensates thoroughly. It lists all verdict values, explains the citation output, and clarifies ambiguous result states. No obvious critical gap remains.

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 the baseline is 3. The description adds value by explaining tolerance_pct's role (overriding implied tolerance, using 1–2 for hallucination detection, default cap at 5) beyond the schema's raw description. This extra semantic context justifies a 4.

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 states a specific verb+resource: natural-language claim verification against authoritative sources, with trigger phrases. It clearly distinguishes itself from siblings by describing a unified pipeline that replaces 4–6 sequential calls, including a structured SEC EDGAR fast path for company-financial claims and a grounded pipeline for other claims.

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 explains the routing logic. However, it does not name alternative sibling tools (e.g., ask_pipeworx_grounded, deep_research) or state when not to use this tool, leaving some ambiguity for an agent choosing among research-related tools.

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
Disambiguation1/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all answer questions or discover data via the same routing engine, with ask_pipeworx_beta explicitly stated to be identical to ask_pipeworx. Polymarket tools (arbitrage, edges, edge_tracker, fill_risk) also blur together, and ai_visibility_check overlaps with scan_competitor_ai_presence.

Naming Consistency2/5

Some clusters are consistent (chargebee_list_*/chargebee_get_*, polymarket_*, pipeworx_*), but the set mixes snake_case with varying verb styles and many unprefixed tools (remember, recall, forget, subscribe, unsubscribe, validate_claim). The 5 Chargebee tools use a clean prefix while the other 31 tools follow several different conventions, making the overall pattern unpredictable.

Tool Count2/5

36 tools is excessively heavy for a server named Chargebee, especially since only 5 tools actually relate to Chargebee. The remaining 31 tools form a broad Pipeworx/prediction-market/utility toolkit that has little connection to the server's apparent billing purpose, making the count feel bloated and unfocused.

Completeness1/5

The Chargebee-specific surface is severely incomplete: it only supports reading customers, subscriptions, and invoices, with no create, update, delete, payment, dunning, coupon, or plan-management operations. The rest of the tools belong to unrelated domains, so the set as a whole has no coherent lifecycle coverage and would leave agents unable to perform even basic Chargebee management tasks.

Resources