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Opencitations

by io.github.pipeworx-io

Bet Research

bet_research
Read-onlyIdempotent

Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the resolver considered), and suggestions[] (explicit re-query hints when the match is fuzzy) — ALWAYS inspect these before trusting the analysis block, because medium/low matches can still surface other fields. PARENT_EVENT EXTRACTOR: when the bet is one leg of a partition (Yankees WS, Romania election), result.parent_event{matched_candidate, top_legs_by_price[], partition_size, placeholders_filtered} gives you the peer prices in one place — that's the headline for elections/championships. NEWS FIELDS: news entries carry _fallback_attempted / _fallback_failed_reason / retry_after_sec when GDELT 429s and GNews backfill ran or failed. SAFETY: low-confidence resolutions short-circuit with status:"low_confidence_match" and suppress analysis fields so agents can't accidentally size on phantom matches. Closed/dead markets that ARE still indexed by Polymarket (yes_price≈0, no volume, no liquidity) return status:"market_closed_or_inactive" and skip fan-out. In practice resolved markets are usually de-indexed and instead surface via the low_confidence_match path above — both routes are BLOCKING, just different mechanisms. Wide-spread markets (>10pp) carry tradeability:"illiquid_wide_spread" + an explanatory note. RESOLUTION-RULE RISK: market.cancellation_rule parses the void/postponement settlement out of the resolution text — refund_50_50 (shares settle flat 50¢ on void; EV-material for any entry away from 50¢, with ev_impact quantified), resolves_no_on_cancel, resolves_yes_on_cancel, carries_to_reschedule, or mentioned_unclear. null means the description never mentions cancellation. Check this before sizing sports/esports/event-occurrence bets — audited arb-bot ledgers show flat-50¢ void settlements are a recurring pure-rules loss.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNoquick = 2-3 evidence sources, thorough = full fan-out. Default thorough.
marketYesPolymarket slug ("will-bitcoin-hit-150k-by-june-30-2026"), full URL ("https://polymarket.com/event/..."), or question text ("Will Bitcoin hit $150k by June 30?")
include_rawNoDefault false. When false (recommended), FRED/FDA/GDELT/Federal-Register evidence is summarized to the few fields agents actually use — keeps responses under ~20KB. Pass true to get full upstream payloads (50KB-500KB) when you need to recompute deltas, cite specific observations, or post-process.

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description richly discloses behavior: fan-out parallelism, resolver contract with match confidence, suppression of fields on low confidence, closed-market handling, news fallback on 429s, and resolution-rule risk. It even quantifies the refund_50_50 scenario. This exceeds what annotations convey and provides critical safety context.

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 long but well-structured with explicit sections (CLASSIFIERS, FAN-OUT EXAMPLES, RESPONSE SHAPES, RESOLVER CONTRACT). It front-loads the core purpose and usage, then dives into details. Almost every sentence adds value, though it could be trimmed without losing essentials. The structure compensates for its size.

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?

Without an output schema, the description carries the full burden of explaining return values. It details response shapes (market, analysis, evidence), resolver fields, parent_event, news fallback fields, safety statuses, and resolution-rule risk. It even highlights the practical de-indexing behavior. For a complex tool, this is exemplary completeness.

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?

The schema covers 100% of parameters with clear descriptions, so baseline is 3. The description adds meaning by explaining how the market parameter is resolved (slug/URL/question text) and that fan-out is parallel. It also clarifies depth differences (quick vs thorough) via examples. Though some of this overlaps with schema, the resolver and response-shape details give extra context for interpreting parameters.

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 a specific verb and resource: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It clearly distinguishes this from sibling tools by focusing on market resolution, classification, and parallel data packs, with examples of input forms. This leaves no ambiguity about what the tool does.

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 provides explicit use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also gives detailed fan-out examples and mentions blocking conditions (low_confidence_match, market_closed_or_inactive). However, it does not explicitly mention when not to use this tool compared to alternatives, e.g., for general research (deep_research) or simple lookups (ask_pipeworx).

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

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical router variants, and entity_profile/compare_entities/recent_changes/ai_visibility_check all inspect companies from overlapping angles. The six Polymarket tools form a tightly-overlapping mini-domain that further crowds the surface.

Naming Consistency3/5

All names are snake_case, but verbs are inconsistently used: many tools are noun phrases (citation_count, entity_profile, polymarket_edges) while others start with verbs (ask_pipeworx, compare_entities, validate_claim). Some prefixes like ask_* and polymarket_* help, but the overall verb/noun pattern is not coherent.

Tool Count2/5

37 tools is well above the typical 3–15 tool scope, and several are redundant variants (three ask_pipeworx modes) or hyper-specific sub-tools (six Polymarket tools). The server name suggests a focused citation service, but only six tools actually address citations, leaving the set bloated with unrelated data query and memory utilities.

Completeness3/5

For a general data-query gateway, the surface is quite broad and covers lookup, profiling, comparisons, subscriptions, and memory. But as an OpenCitations server it lacks a way to discover papers by topic and the breadth of the other domains is unwieldy and unowned—so notable gaps exist in any plausible stated purpose.

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