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Open Notify

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent, and the description adds significant behavioral nuance beyond that: could_not_verify means the check did not happen and carries verification_error, unsupported means no source covers it, and verdicts include a grounded or structured value with citation. This is exactly the kind of context an agent needs to interpret results safely.

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 fairly long but front-loaded with trigger phrases and organized into clear sections: purpose, routing, return value, and important caveats. Every sentence adds value, though the density is high and could be slightly tighter without losing critical details.

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 the tool's complexity and lack of an output schema, the description is remarkably complete: it enumerates the possible verdicts, explains the error field, clarifies the distinction between could_not_verify and unsupported, and notes the efficiency gain over a multi-call pipeline. With only two well-documented parameters, no essential information is missing.

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 schema already describes both parameters with high coverage, the description adds operational guidance for tolerance_pct: it overrides the tolerance implied by wording, is capped at 5 by default, and suggests setting 1–2 for hallucination detection. It also gives concrete claim examples for the claim parameter.

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 natural-language trigger phrases and clearly states the tool's function: natural-language claim verification against authoritative sources. It distinguishes itself from sibling tools by explicitly noting it replaces 4–6 sequential calls and by describing the routing between SEC EDGAR and the grounded pipeline.

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?

It explicitly says to use the tool whenever the agent needs to check whether something a user said is factually correct. It also provides concrete routing rules: company-financial claims go through the SEC EDGAR fast path, while any other factual claim falls through to the grounded pipeline, with clear semantics for could_not_verify vs. unsupported.

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

A3.6/5.0
Disambiguation2/5

The three ask_pipeworx variants are near-identical (the beta is currently an exact copy of the stable router), and ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim all route natural-language questions to the same underlying source catalog. The six polymarket_* tools also heavily overlap in opportunity detection, though some clusters like memory and subscriptions are clearly separated.

Naming Consistency3/5

The naming is mostly snake_case but mixes conventions: verb_noun (list_subscriptions, resolve_entity), noun-first (entity_profile, bet_research), metadata-style prefixes (pipeworx_trending, polymarket_edges), and bare verbs (remember, recall, forget). It is readable but does not follow one predictable pattern across the set.

Tool Count2/5

33 tools is excessive for a server nominally named Open Notify, and the count is inflated by redundant ask_pipeworx variants and six closely-related Polymarket tools. The broad data-gateway scope could justify a large catalog, but the set feels bloated and unfocused rather than deliberately scaled.

Completeness2/5

For the apparent Open Notify domain, only astros and iss_now fit, and core ISS functionality like pass predictions is missing. The wider data-lookup surface is extensive, but the inclusion of unrelated memory, subscription, npm-scanning, and llms.txt tools means no single domain gets coherent lifecycle coverage.

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