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Openparliament Ca

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

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive nature, so the description goes far beyond by disclosing the dual routing logic, the exact verdict set, and the critical semantics of 'could_not_verify' (not evidence) versus 'unsupported' (no source coverage). It also warns callers not to present 'could_not_verify' as evidence, which is crucial behavioral context not inferable from annotations.

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 lengthy but each section earns its place: user-phrased examples, routing explanation, verdict list, and caller caution. It is front-loaded with intuitive trigger phrases and usage instructions before diving into technical details. Slightly verbose, but the complexity of the tool justifies it; no filler sentences.

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 fully compensates by enumerating all possible verdicts, explaining the meaning of each ambiguous outcome, and describing the nature of evidence returned (pipeworx:// citation). It also covers the two distinct input scenarios and the behavioral implications of the tolerance parameter, making the description self-sufficient for an agent to predict outcomes.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema covers both parameters with detailed descriptions (claim example, tolerance range, default cap). The description adds context about tolerance overriding implied wording and the 1–2% recommendation for hallucination detection, but this is moderately additive. Since schema coverage is 100%, a baseline of 3 is appropriate.

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 verification system with a specific verb ('validate', 'verify', 'fact check') and resource ('against authoritative sources'). It explicitly distinguishes itself from siblings by describing the two routing paths (SEC EDGAR/XBRL for company-financial claims, grounded pipeline for others) and stating it 'replaces 4–6 sequential calls', making its unique scope unmistakable.

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 gives direct usage guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also separates when to use the structured path vs. the grounded path. However, it does not explicitly name alternative sibling tools or state when *not* to use it, which would elevate to 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

B3.1/5.0
Disambiguation2/5

The tool set mixes two distinct domains: 8 Canadian Parliament tools and 30 Pipeworx data tools. Within each domain, tools are somewhat distinct, but the overall mix creates confusion as agents cannot tell if a tool is for parliament or general data lookup.

Naming Consistency2/5

OpenParliament tools follow a consistent verb_noun pattern (list_*, get_*), but Pipeworx tools use varied conventions (e.g., 'remember', 'discover_tools', 'ask_pipeworx'). The lack of a unified naming scheme across the set reduces predictability.

Tool Count2/5

38 tools is excessive for a Canadian Parliament server. The core parliament tools (8) are well-scoped, but the addition of 30 unrelated Pipeworx tools makes the count bloated and inappropriate for the stated domain.

Completeness1/5

For the Canadian Parliament domain, coverage is adequate but lacks topic search and detailed legislative history. However, the server is dominated by Pipeworx tools, which are out of scope, making the overall surface severely incomplete for the implied purpose.

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