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Remoteok

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?

Beyond the non-destructive/read-only annotations, the description discloses the fast-path versus grounded pipeline selection, the exact meaning of 'could_not_verify' (with verification_error), and 'unsupported', plus an explicit caller warning that could_not_verify must not be presented as evidence. This greatly exceeds annotation coverage and exposes potential pitfalls.

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 long but every sentence carries weight: trigger phrases, usage rule, pipeline routing, return semantics, and the critical edge-case warning. It is layered logically and front-loads the purpose, achieving high information density with zero filler.

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 enumerates the verdict set and clarifies the nuanced distinction between could_not_verify and unsupported. It covers both execution paths and even explains the tolerance math rule, making the tool self-contained for an agent to invoke and interpret results correctly.

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% coverage for both parameters with detailed descriptions and examples, which sets the baseline at 3. The description adds no new parameter-level detail; it only re-emphasizes the percent-delta math already covered by the schema. This is adequate but not additive.

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 concrete trigger phrases ('fact check', 'verify the claim that…') and states the exact function: natural-language claim verification against authoritative sources. It differentiates the two execution pipelines (SEC/XBRL for financial, grounded for all else) and lists the output verdicts, making the purpose unmistakable and distinct from sibling ask/verify tools.

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 an explicit when-to-use rule: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the internal routing for financial vs non-financial claims. However, it does not name specific sibling tools to prefer instead or list exclusions, so it lacks the when-not-to/alternatives dimension of a full 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

A3.9/5.0
Disambiguation2/5

Several tool clusters have fuzzy boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer questions through the same routing layer, while polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and bet_research all address prediction-market edges. Even with detailed descriptions, an agent can easily select the wrong variant.

Naming Consistency3/5

Names are uniformly lowercase snake_case and many follow a verb_noun pattern (list_jobs, search_jobs, resolve_entity, validate_claim), but there are numerous noun-first and adjective-first exceptions (entity_profile, recent_alerts, pipeworx_trending, polymarket_arbitrage) plus bare verbs (remember, forget, recall, subscribe). The pattern is readable but not consistently applied.

Tool Count2/5

34 tools is well above the well-scoped range, and the vast majority are not about the server's RemoteOK namesake. The set appears to merge several distinct domains (Pipeworx data research, Polymarket betting, RemoteOK jobs, memory utilities) into one oversized surface, making it feel more like a bundled platform than a focused server.

Completeness4/5

For the dominant Pipeworx/data-research theme, coverage is strong: question answering, grounded verification, entity profiles, comparisons, change feeds, discovery, memory, subscriptions, and citation-based research are all present. The RemoteOK job subset covers list/search/get without obvious dead ends. Minor gaps exist (no direct citation-URI fetcher, no job alert subscriptions), but agents can work around them.

Resources