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Pulsedive

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

A4.5/5.0
Behavior5/5

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

The description discloses key behaviors beyond the annotations: the special meaning of could_not_verify (with verification_error) and its non-evidentiary nature, the unsupported fallback, and the automatic routing. These are important caveats that the annotations do not cover, making the tool's behavior transparent.

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 front-loaded with user-facing trigger phrases and use cases, then logically progresses through routing, output, and critical callers' caveats. Every sentence adds necessary information for correct invocation, and the structure makes it easy to scan.

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?

Since there is no output schema, the description compensates by fully explaining the return shape (verdicts, value with citation, reasoning) and the critical distinction between could_not_verify and unsupported. Combined with rich annotations and schema coverage, the description leaves no major operational gaps.

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?

The input schema already provides 100% descriptive coverage for both parameters, including the purpose and default behavior of tolerance_pct. The description adds behavioral context about exact percent-delta math but does not materially enhance parameter semantics beyond the schema, so the baseline 3 applies.

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 defines the tool as conducting natural-language claim verification against authoritative sources, using explicit verb 'verify' and listing exact verdicts. It also distinguishes from sibling tools by noting it replaces 4–6 sequential calls, making its 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?

Explicitly states the trigger: whenever the agent needs to check whether something a user said is factually correct. It details routing logic for company-financial vs. other claims, giving clear context. However, it does not explicitly state when NOT to use it (e.g., for open-ended research), so it lacks a full exclusion boundary.

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

Many tools have overlapping purposes, e.g., five 'ask_pipeworx' variants and multiple prediction market tools with subtle distinctions. An agent would struggle to pick the correct tool without careful reading of long descriptions.

Naming Consistency4/5

Most tools follow a verb_noun pattern with domain prefixes (pipeworx_, polymarket_, pulsedive_), but a few standalone verbs (forget, recall) break the pattern slightly. Overall consistent within groups.

Tool Count3/5

33 tools is on the heavy side, but the server covers a broad range of domains (data querying, prediction markets, security, subscriptions). The count is borderline but not excessive given the scope.

Completeness4/5

The tool set covers a wide array of operations: querying, entity analysis, comparisons, prediction market edge detection, subscriptions, memory, and scanning. Minor gaps exist (e.g., limited to Polymarket/Kalshi for prediction markets), but overall the surface is comprehensive.

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