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Google_calendar

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

A4.8/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description reveals critical behavior: the two distinct verification pipelines, the full verdict vocabulary, the distinction between could_not_verify (a failure, not evidence) and unsupported, and the inclusion of citations and reasoning. This context is not inferable from annotations and prevents caller misuse.

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 detailed but every sentence adds value. It front-loads user-phrase triggers, then explains the dual pipelines, verdicts, caller caveats, and the composite nature of the tool. Structure is logical and efficient for the complexity involved.

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?

Without an output schema, the description thoroughly explains return values (verdict, actual value, citation, reasoning) and error semantics. It covers both company-financial and general claims, the tolerance override, and important caller warnings, making the tool's behavior fully comprehensible in context.

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?

Schema coverage is 100%, and the description adds substantial semantic depth: tolerance_pct overrides the implied tolerance, has a 0.5–50 range, default is capped at 5, and setting 1–2 is recommended for hallucination detection. This goes far beyond the schema's syntax.

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 query phrases and a clear definition: 'natural-language claim verification against authoritative sources.' It distinguishes the tool from general Q&A siblings by stating 'Use whenever the agent needs to check whether something a user said is factually correct.'

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 states when to use (claim verification) and provides detailed routing rules: company-financial claims go through the SEC/XBRL fast path, all other claims fall through to the grounded pipeline. It does not explicitly name sibling alternatives or state when not to use it, but the guidance is strong and covers scoped use cases.

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

The tool set is a mix of 5 Google Calendar tools and 31 unrelated Pipeworx tools (e.g., ask_pipeworx, deep_research, entity_profile). Even within Pipeworx, tools like ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded have highly overlapping purposes, making it very difficult for an agent to distinguish which tool to use.

Naming Consistency1/5

Naming conventions are chaotic. The Google Calendar tools follow a consistent gcal_ prefix, but the rest use various patterns: pipeworx_*, polymarket_*, single words (remember, recall), and others (bet_research, scan_dependency). There is no overarching pattern.

Tool Count1/5

With 36 tools, the count is high, but only 5 are relevant to Google Calendar. The remaining 31 tools are from a completely different domain (Pipeworx data access, Polymarket betting, etc.), making the tool count severely inappropriate for the server's stated purpose.

Completeness2/5

For Google Calendar, the tools provide basic CRUD (create, get, list, search, list_calendars) but lack update and delete functionality. The vast number of unrelated tools does not compensate for these gaps. The overall surface is incomplete for the calendar domain.

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