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Glama

timezone

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?

The description adds significant behavioral context beyond the annotations: it distinguishes 'could_not_verify' from 'unsupported', warns callers not to treat 'could_not_verify' as evidence, and details the structured vs. grounded pipeline paths. This is critical operational knowledge that annotations alone do not provide, and it is clearly conveyed.

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 information-dense and well-structured, front-loaded with user intents and purpose. It includes an 'IMPORTANT for callers' callout for error semantics. It is somewhat long (approximately 200 words), but every sentence contributes to understanding the tool's behavior, so it earns a 4 rather than a 5.

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?

Even without an output schema, the description fully discloses the return format (verdict list, actual value with citation, reasoning) and the meaning of each verdict type, including the critical 'could_not_verify' and 'unsupported' cases. It also explains the two processing paths, making the tool's behavior transparent and complete for a complex verification tool.

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?

Although the input schema already describes both parameters with 100% coverage, the description enriches their meaning. It explains that 'tolerance_pct' overrides the implied tolerance from the claim wording, suggests values for hallucination detection, and provides concrete examples for the 'claim' parameter. This goes well beyond the schema's basic documentation.

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 clearly identifies the tool as a natural-language claim verifier with specific verbs ('validate', 'verify', 'fact check') and the resource ('claim'). It provides example user phrasings and explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct,' distinguishing it from sibling research/Q&A tools. The financial vs. other claim routing further clarifies scope.

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 gives explicit when-to-use guidance ('Use whenever the agent needs to check whether something a user said is factually correct') and explains the internal routing for financial vs. other claims. It also notes what the tool replaces (4–6 sequential calls). However, it does not explicitly name alternative sibling tools or provide when-not-to-use exclusions, so it falls short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All timezone toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the multiple 'ask_pipeworx' variants and several Polymarket utilities. The server mixes a few timezone tools with a large collection of unrelated data lookup and analysis tools, making it hard for an agent to distinguish which tool to use for a given task.

Naming Consistency2/5

While all tool names use snake_case, the verbs are highly inconsistent (e.g., 'ask_pipeworx', 'convert_time', 'discover_tools', 'validate_claim'). There is no clear pattern or predictable naming convention across the set.

Tool Count2/5

The server name 'timezone' suggests a narrow domain, but it contains 35 tools, the vast majority of which are unrelated to timezones. This is far too many for the implied scope, and the server seems to be a dumping ground for various services.

Completeness1/5

For a timezone server, only 4 tools (convert_time, get_time_by_ip, get_time_by_timezone, list_timezones) are relevant. Critical timezone functionality like time zone conversions with arbitrary offsets, DST handling, or time zone by coordinates is missing. The remaining tools are completely unrelated to the server's stated purpose.

Resources