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Storting No

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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds substantial behavioral insight beyond these: the routing behavior for company financial claims vs. other facts, the semantic difference between could_not_verify and unsupported, the verification_error structure, and the efficiency claim of replacing 4–6 sequential calls. No contradictions with annotations.

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 about 150 words but every sentence carries operational value: purpose, examples, routing, return types, error semantics, and performance rationale. It is front-loaded with the core definition and examples, then layered with important details. There is no fluff or redundant restating of schema/annotations.

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?

With no output schema, the description takes full responsibility for explaining return values and behavior. It lists the verdict types, mentions the pipeworx:// citation, and clarifies the key distinction between could_not_verify and unsupported. It also covers both pipeline paths and the tolerance parameter, making it a complete guide for an agent to invoke and interpret 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 schema already provides full descriptions for both claim and tolerance_pct (100% coverage). The description adds valuable extra guidance: for tolerance_pct it explains how to use it for hallucination detection (set 1–2) and the default cap of 5, which goes beyond the schema. For claim it provides concrete examples of natural-language inputs.

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 and specifically states the tool's purpose: natural-language claim verification against authoritative sources. It provides example phrasings like 'fact check' and 'verify the claim that…' and distinguishes itself from sibling tools like ask_pipeworx_grounded by focusing on producing a verdict. The two-path routing (SEC EDGAR vs. grounded pipeline) further clarifies its specific 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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct,' which provides clear context. It also explains the two categories of claims and includes an important caveat about interpreting could_not_verify. However, it does not explicitly name alternative tools or provide when-not-to-use exclusions, so it lacks the full explicitness of 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
Disambiguation2/5

Several tools are near-duplicates: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route questions to the same underlying toolset with only minor differences. Additionally, the five polymarket_* tools overlap significantly in purpose, and the mix of Storting parliament tools with a general-purpose data platform creates confusion about which tool is appropriate.

Naming Consistency2/5

Tool names use snake_case but with inconsistent conventions. Some are verb-first (get_, list_, discover_, validate_), while others are noun-first (entity_profile, bet_research, recent_alerts). There are predictable prefixes like ask_pipeworx and polymarket_, but overall the naming pattern is not uniform, making it harder to predict tool names.

Tool Count2/5

With 38 tools, the count exceeds the 25-tool threshold for 'too many.' Many tools are unrelated to the server name 'Storting No' (which implies a Norwegian parliament focus), and the broad range of data-research and prediction-market tools feels bloated for the apparent scope. A smaller, more focused set would improve coherence.

Completeness3/5

The Storting-related tools cover the main parliamentary entities (cases, parties, representatives, sessions, votes) and include an export fallback for any additional data.stortinget.no resource. The Pipeworx side has meta-tools (ask, discover, suggest) and specialized analyses (entity_profile, validate_claim, polymarket_*). However, there are notable gaps, such as no direct tool for searching parliamentary speeches or committee documents without relying on the generic export, and the mixed domains leave some workflows incomplete.

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