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sunrisesunset

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

A4.5/5.0
Behavior5/5

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

Annotations already note readOnly/idempotent/openWorld, and the description adds critical behavior beyond that: the exact meaning of could_not_verify (check failed, not evidence) and unsupported (no source covered), plus the two execution paths (SEC EDGAR fast path vs. grounded pipeline). The warning to never show could_not_verify as evidence is essential and goes far beyond the annotation hints.

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 a bit long but well-structured: it starts with trigger phrases and a one-sentence purpose, then covers routing, return values, and an important caller warning. Each sentence contributes meaningful detail. It could be slightly tighter, but the density is justified by the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by enumerating the verdict set (confirmed, approximately_correct, refuted, inconclusive, unsupported, could_not_verify) and stating the return components (actual value with citation, reasoning). It also clarifies error semantics and routing. It does not fully outline the exact response JSON structure, but the provided detail is sufficient for an agent to interpret the result.

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?

The input schema already describes claim and tolerance_pct clearly with coverage of 100%. The description adds extra value by explaining how tolerance_pct overrides the wording-implied default, caps at 5, and recommends 1–2 for hallucination detection. This goes beyond the schema's basic type/description, effectively guiding parameter use.

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 natural-language claim verification against authoritative sources, with explicit trigger phrases and a defined output (verdict + evidence). It distinguishes itself from siblings by focusing on fact-checking with verdicts like confirmed/refuted, and by noting it replaces multiple sequential calls (NL parsing → entity resolution → data lookup → comparison).

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 for company-financial vs. other claims. It does not name specific alternative tools, but the note about replacing 4–6 sequential calls gives guidance to prefer this tool over composing multiple steps. Missing explicit exclusions or named alternatives, so not a 5.

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

A3.7/5.0
Disambiguation4/5

Most tools have distinct purposes with detailed descriptions, but there is overlap among closely related Polymarket tools (e.g., bet_research, polymarket_edges, polymarket_arbitrage) and between ask_pipeworx and ask_pipeworx_grounded, which could cause misselection.

Naming Consistency3/5

Tool names mix verb_noun, noun_verb, and descriptive phrases inconsistently (e.g., get_times vs listen_subscriptions vs bet_research), and while many use underscores, conventions vary, making patterns hard to predict.

Tool Count3/5

With 32 tools, the server feels overloaded, covering distinct domains (sunrise, Pipeworx, Polymarket, memory, subscriptions) under one name, making it a 'Swiss army knife' rather than a focused toolset.

Completeness2/5

The server name suggests a narrow domain (sunrise/sunset) but only 2 of 32 tools address it, ignoring related weather data. While other domains are covered, operations lack depth (e.g., no subscription updates, no memory search).

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