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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?

As annotations indicate read-only, idempotent, non-destructive behavior, the description adds extensive behavioral context: market resolution, classification, fan-out, response shapes, resolver contract, parent event extraction, news fallback, safety cutoffs, wide spread handling, and resolution rules. No contradiction with 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?

Description is long but not wasteful; each section adds necessary context for correct usage. Could benefit from better organization (e.g., sections or bullet points) but front-loads essential purpose and usage, then dives into details.

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?

Despite no output schema, the description comprehensively explains response structure, statuses, edge cases, and safety considerations. Covers resolution confidence, parent events, news fallback, closed markets, wide spreads, and cancellation rules—all critical for correct interpretation.

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?

Schema provides 100% coverage with descriptions, so baseline is 3. Description adds meaningful context: market can be slug, URL, or question; depth explains quick vs thorough; include_raw explains when to use true vs false. This extra detail justifies a slightly higher score.

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?

State explicitly that the tool researches Polymarket bets by pulling Pipeworx data. Lists use cases ('should I bet on X', 'what does the data say about Y', 'is there edge in Z'). Distinguishes from siblings like polymarket_arbitrage and polymarket_edges by focusing on research and evidence.

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?

Provides clear when-to-use guidance with example queries and classifiers. However, does not explicitly state when NOT to use or suggest alternative tools. The detail on safety and low-confidence matches helps agents understand limitations.

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

A3.8/5.0
Disambiguation2/5

Several clusters of near-duplicates force careful reading: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded overlap heavily; the five polymarket tools share edge-detection and arbitrage territory; and ai_visibility_check vs scan_competitor_ai_presence are easy to confuse. discover_tools, suggest_questions, and pipeworx_trending also compete as discovery/onboarding entry points.

Naming Consistency3/5

All names are lowercase snake_case, so there is surface consistency, but the structural pattern is mixed: verb_noun (list_feeds, read_feed, resolve_entity), noun_verb (ai_visibility_check), noun_noun (entity_profile, polymarket_fill_risk), and bare verbs (remember, forget). Prefixes like ask_pipeworx and polymarket create local order, but no server-wide naming convention holds.

Tool Count2/5

34 tools for a server named 'Gaming Feeds' is heavily over-scoped; only list_feeds, read_feed, and fetch_feed actually serve that purpose. The remaining ~25 tools constitute an unrelated Pipeworx data platform covering queries, prediction markets, memory, subscriptions, and feedback, making the count feel like a bundled mega-server rather than a focused toolset.

Completeness3/5

For the nominal gaming-feed domain, list/read/fetch plus keyword filtering covers basic consumption, but there is no cross-feed search, feed management, or feed-specific subscription support (subscribe only handles SEC, Polymarket, and FRED streams). The broader data/research surface is comprehensive internally, but that completeness belongs to a different server's purpose.