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Glama

Timeapi Io

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

A4.7/5.0
Behavior5/5

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

Despite annotations already declaring read-only, open-world, idempotent, and non-destructive behavior, the description adds substantial behavioral nuance. It explains the verdict enum, especially the crucial caveat that could_not_verify is a failed check rather than evidence, and outlines the routing between structured SEC EDGAR and grounded pipeline. This contextual depth goes well beyond what annotations provide.

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 thorough and well-front-loaded, starting with purpose and trigger phrases. Each sentence contributes, but the listing of trigger phrases and the redundant closing about replacing sequential calls could be considered slightly verbose. It is structured logically from purpose to behavior to caveats, earning a strong score.

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 there is no output schema, the description fully explains the return structure: verdict, actual value with citation, and reasoning. It also details the two failure modes (could_not_verify vs unsupported) and their implications. With only two parameters and rich annotations, this description is complete for an agent to select and invoke the tool 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?

The input schema already covers both parameters (claim and tolerance_pct) with descriptions. The tool description enhances this by providing a concrete example for claim and detailed guidance for tolerance_pct (e.g., override default, set 1–2 for hallucination detection, capped at 5). This adds meaningful semantic guidance beyond the 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 opens with explicit trigger phrases ("Is it true that…", "fact check", "verify the claim") and states the core function: natural-language claim verification against authoritative sources. It distinguishes from siblings by specifying it replaces 4–6 sequential calls and handles both company-financial and other claims. The specific verb+resource combination (validate + claim) is clear and unambiguous.

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 clearly states when to use: "Use whenever the agent needs to check whether something a user said is factually correct." It also differentiates between company-financial and other claims via routing details. However, it does not explicitly mention when not to use or name alternative tools beyond the implicit "grounded pipeline" fallback, so it lacks explicit exclusions.

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

B3.2/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer natural-language data questions, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The current_time* variants and multiple Polymarket scanners also create real selection ambiguity, though the memory and subscription tools are clearly distinct.

Naming Consistency3/5

Names are mostly lowercase snake_case and readable, but there is no consistent verb_noun pattern: some are imperative (ask_pipeworx, compare_entities, generate_llms_txt) while others are object-first (entity_profile, recent_changes, pipeworx_trending). Subfamilies like current_time* and polymarket_* are internally consistent, but the overall set follows no predictable convention.

Tool Count2/5

Forty tools is far too many for a server named Timeapi Io; only about nine tools actually relate to time zones and current time. The rest form a sprawling Pipeworx research, prediction-market, memory, and subscription platform, making this a mega-bundle rather than a well-scoped toolset.

Completeness3/5

The time-related surface covers current time, zone conversion, zone metadata, and ISO parsing, but lacks common date math or general formatting operations. The Pipeworx side is quite complete for research and fact-checking, but the mixed domain makes coverage uneven and hard to reason about as a single coherent service.

Resources