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Glama

Zoom

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

A5/5.0
Behavior5/5

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

Discloses verdict taxonomy, the crucial distinction between could_not_verify (check did not happen) and unsupported (no source), and the verification_error object. This complements annotations (readOnly, openWorld, idempotent) without contradiction.

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?

The description is long but efficiently structured: leads with query phrasings, then purpose, pathways, return format, and a highlighted caller warning. Every sentence adds operational value.

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?

Despite lacking an output schema, the description fully covers return values (verdicts, actual value, citation, reasoning) and error semantics. It also explains the dual-pipeline behavior, making it self-sufficient for an agent to use correctly.

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

Parameters5/5

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

While schema covers both parameters, the description adds: tolerance_pct overrides implied tolerance, recommended 1–2 for hallucination detection, default capped at 5. Claim examples clarify expected input format beyond schema.

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: verifying natural-language claims against authoritative sources, with distinct execution paths for company-financial vs other claims. It distinguishes from generic Q&A by returning structured verdicts and evidence.

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?

Explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and details routing rules (SEC EDGAR fast path vs grounded pipeline). It also notes it replaces 4–6 sequential calls, guiding agents away from multi-step alternatives.

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 set mixes two entirely different domains: 4 Zoom tools and 31 Pipeworx/prediction-market tools. Within the Pipeworx side, ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions overlap heavily, as do the five polymarket_* tools. An agent could easily select the wrong variant despite the long descriptions.

Naming Consistency2/5

The Zoom tools follow a clean zoom_* pattern, and there are subfamilies like ask_pipeworx_* and polymarket_*, but the overall set is a mix of snake_case verbs, bare nouns, and inconsistent styles (bet_research, entity_profile, generate_llms_txt, list_subscriptions, pipeworx_feedback, validate_claim). No single predictable convention governs the server's tool names.

Tool Count2/5

35 tools is heavy for any server, and the vast majority are unrelated to the server's declared 'Zoom' purpose. Only 4 of 35 tools actually concern Zoom, making the count both bloated and mismatched. A focused Zoom server would need far fewer tools; a Pipeworx data server would need a different name.

Completeness2/5

For a Zoom server, the surface is critically incomplete: only read-only list/get operations exist for meetings, recordings, and the current user, with no create, update, delete, or invite functionality. The Pipeworx side is comparatively rich and complete, but that does not serve the Zoom domain implied by the server name, so significant gaps remain for the apparent purpose.

Resources