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Jira

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.

TDQS

A4.8/5.0
Behavior5/5

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

Beyond annotations (readOnly/openWorld/idempotent), the description discloses crucial behavioral details: could_not_verify means the check did not happen and must not be treated as evidence; unsupported means no source coverage. Also exposes the internal pipeline and return structure (verdict, value, citation, reasoning).

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?

Description is long but every sentence provides valuable information: trigger phrases, routing, verdict types, error semantics, and efficiency claim. Structure is logical and front-loaded with the core purpose. Slightly overlong, but appropriate for the tool's complexity.

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 return values: verdict enum with six possible values, actual value with citation, and reasoning. Also covers error conditions (verification_error) and distinguishes edge cases. All necessary operational context is present.

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?

Input schema already documents both parameters well (claim with examples, tolerance_pct with range and default). Description adds meaningful nuance: tolerance_pct overrides implied wording and is capped at 5 by default, plus guidance for hallucination detection. This goes 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?

Description clearly identifies the tool as natural-language claim verification against authoritative sources, with explicit trigger phrases like 'fact check' and 'verify the claim that.' It distinguishes itself from sibling research tools by focusing on true/false verdicts and providing structured verdict categories.

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?

States explicitly to use when the agent needs to check factual correctness of a user statement. Also explains routing logic: financial claims via SEC EDGAR/XBRL, any other claims via grounded pipeline. This gives clear, actionable guidance and differentiates from alternatives.

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
Disambiguation3/5

Tool purposes are largely distinct across domains (Jira, data queries, prediction markets), but there is overlap within domains, e.g., ask_pipeworx vs ask_pipeworx_grounded and multiple Polymarket edge analysis tools.

Naming Consistency2/5

Naming mixes multiple conventions: jira_ prefix, pipeworx_ prefix, polymarket_ prefix, and plain verbs (remember, forget, recall). No consistent pattern across the entire set.

Tool Count2/5

34 tools is excessive for a server named 'Jira' which only has 4 Jira-specific tools. The majority are unrelated Pipeworx and Polymarket tools, making the scope bloated.

Completeness2/5

Jira-specific tools are incomplete (missing create, update, delete). While Pipeworx and Polymarket tools are comprehensive, they are out of place for a Jira server, leaving gaps in its stated purpose.

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